Dynamic lung image intelligent detection method and device, program product and medical equipment

Abstract

The present disclosure relates to a dynamic lung image intelligent detection method and device, a program product and a medical equipment, and relates to the technical field of detection. The method comprises: acquiring corresponding dynamic right lung and left lung diaphragm sequences in a breathing process; determining a right lung reference diaphragm length as the shortest length in a first length sequence corresponding to the dynamic right lung diaphragm sequence; determining other right lung diaphragms as normal or abnormal right diaphragms based on the right lung reference diaphragm length, first lengths in the first length sequence except the right lung reference diaphragm length, and a first preset length difference; determining a left lung reference diaphragm length as the shortest length in a second length sequence corresponding to the dynamic left lung diaphragm sequence; and determining other left lung diaphragms as normal or abnormal left diaphragms based on the left lung reference diaphragm length, second lengths in the second length sequence except the left lung reference diaphragm length, and a second preset length difference.

Description

Technical Field

[0001] This disclosure relates to the field of X-ray two-dimensional chest image detection technology, and in particular to a dynamic lung image intelligent detection method and device, program product and medical equipment. Background Technology

[0002] X-rays are the most widely used primary imaging technique in routine chest and bone radiography due to their widespread availability, low cost, fast imaging speed, and ease of acquisition. Specifically, by projecting the captured human body directly onto a two-dimensional planar image, digital X-ray images can be obtained within seconds of exposure. Therefore, it has become the preferred imaging device in clinical practice for improving efficiency and facilitating initial chest diagnosis of critically ill and / or emergency patients.

[0003] Compared to inspiratory and expiratory chest CT images (two time points), chest fluoroscopy or spot chest images include more time points during free breathing. Therefore, this makes a significant contribution to the dynamic quantitative analysis of lung movement functions, such as hemidiaphragmatic movement.

[0004] Specifically, Tanaka et al. assessed the correlation between diaphragmatic motion parameters and vital capacity. Meanwhile, Yamada et al. used dynamic chest radiography (DCR) to assess mean diaphragmatic displacement in healthy volunteers and differences in tidal diaphragmatic motion between COPD (Chronic Obstructive Pulmonary Disease) and healthy controls. Subsequently, Yamada et al. further evaluated the correlation between diaphragmatic motion and anthropometric measurements. Furthermore, Hida et al. assessed diaphragmatic motion in a standing position during forced breathing and evaluated its relationship with demographics and pulmonary function tests. Hida et al. subsequently further evaluated differences in diaphragmatic motion velocity and displacement between COPD and controls, as well as the correlation between pulmonary function tests and diaphragmatic motion. Additionally, FitzMaurice et al. described changes in diaphragmatic motion and lung area before and after modulatory therapy in adults with cystic fibrotic bronchiectasis using DCR. Subsequently, FitzMaurice et al. further described diaphragmatic movement in patients treated with DCR for hemidiaphragmatic paralysis, as well as diaphragmatic joint movement in patients undergoing treatment for cystic fibrosis-related bronchiectasis. Furthermore, Chen et al. used DCR to quantitatively assess diaphragmatic movement during forced breathing in patients with chronic obstructive pulmonary disease. Therefore, accurate hemidiaphragmatic detection in dynamic multi-frame 2D chest X-ray images corresponding to DCR images during respiration is crucial for accurately assessing diaphragmatic function.

[0005] However, due to lung field deformation in DCR leading to abnormal lung field morphology, existing methods for measuring the hemidiaphragm often result in abnormal measurements of the hemidiaphragm corresponding to the lung field. Therefore, it is necessary to propose an optimized algorithm to ensure the accuracy of hemidiaphragm measurements on dynamic chest X-ray (dynamic multi-frame two-dimensional chest X-ray images) for subsequent quantitative analysis. The primary task in optimizing the localization of the hemidiaphragm is to detect normal or abnormal diaphragms to facilitate subsequent optimization of the localization of abnormal diaphragms. Summary of the Invention

[0006] This disclosure presents a technical solution for a dynamic lung image intelligent detection method and device, program product, and medical device.

[0007] According to one aspect of this disclosure, a dynamic lung image intelligent detection method is provided, comprising:

[0008] Acquire at least one diaphragm sequence corresponding to a dynamic right lung diaphragm sequence (dynamic right diaphragm sequence) and a dynamic left lung diaphragm sequence (dynamic left diaphragm sequence) from multiple dynamic two-dimensional chest X-ray images during respiration.

[0009] The shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence is determined as the right lung baseline diaphragm length; based on the right lung baseline diaphragm length, the difference between the first length in the first length sequence excluding the right lung baseline diaphragm length and the first preset length, the other right lung diaphragms are respectively determined as normal right diaphragms (normal right lung diaphragm) or abnormal right diaphragms (normal right lung diaphragm); and / or, the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence is determined as the left lung baseline diaphragm length; based on the left lung baseline diaphragm length, the difference between the second length in the second length sequence excluding the left lung baseline diaphragm length and the second preset length, the other left lung diaphragms are respectively determined as normal left diaphragms (normal left lung diaphragm) or abnormal left diaphragms (abnormal left lung diaphragm).

[0010] Preferably, the method of determining the right lung baseline diaphragm length by the shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence; and determining the other right lung diaphragms as normal or abnormal right diaphragms based on the right lung baseline diaphragm length, the first length in the first length sequence excluding the right lung baseline diaphragm length, and the difference between the first length and the first preset length, respectively, includes: determining the right lung baseline diaphragm by the shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence; and determining the other right lung diaphragms as normal or abnormal right diaphragms based on the first baseline length corresponding to the right lung baseline diaphragm, the first length in the dynamic right lung diaphragm sequence excluding the right lung baseline diaphragm, and the difference between the first length and the first preset length, respectively.

[0011] Preferably, the method of determining the left lung baseline diaphragm length by the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; and determining the other left lung diaphragms as normal or abnormal based on the difference between the left lung baseline diaphragm length, the second length in the second length sequence excluding the left lung baseline diaphragm length, and the second preset length, includes: determining the left lung baseline diaphragm by the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; and determining the other left lung diaphragms as normal or abnormal based on the difference between the second baseline length corresponding to the left lung baseline diaphragm, the second length in the dynamic left lung diaphragm sequence excluding the left lung baseline diaphragm, and the second preset length, respectively.

[0012] Preferably, the method for determining the first length sequence corresponding to the dynamic right lung diaphragm sequence includes: respectively counting the sum of the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence; respectively obtaining the first length sequence corresponding to the dynamic right lung diaphragm sequence based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel.

[0013] Preferably, the method for determining the first length sequence corresponding to the dynamic right lung diaphragm sequence based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of first pixel values ​​corresponding to each right lung diaphragm by the area corresponding to each pixel to obtain the first length sequence corresponding to the dynamic right lung diaphragm sequence.

[0014] Preferably, the method for determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: respectively counting the sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence; respectively obtaining the second length sequence corresponding to the dynamic left lung diaphragm sequence based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

[0015] Preferably, the method for determining the second length sequence corresponding to the dynamic left lung diaphragm sequence based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of second pixel values ​​corresponding to each left lung diaphragm by the area corresponding to each pixel to obtain the second length sequence corresponding to the dynamic left lung diaphragm sequence.

[0016] Preferably, before acquiring at least one diaphragm sequence of the dynamic right lung diaphragm sequence and the dynamic left lung diaphragm sequence corresponding to the dynamic multiple X-ray two-dimensional chest images during respiration, the method for determining the corresponding dynamic right lung diaphragm sequence and / or dynamic left lung diaphragm sequence based on the dynamic multiple X-ray two-dimensional chest images during respiration includes: acquiring at least one masked edge image sequence of the right lung masked edge image sequence (right lung masked edge image) and the left lung masked edge image sequence (left lung masked edge image) corresponding to the dynamic multiple X-ray two-dimensional chest images (dynamic X-ray two-dimensional chest images to be located during respiration / dynamic lung images during respiration); determining the corresponding dynamic right lung diaphragm sequence and / or dynamic left lung diaphragm sequence based on the dynamic multiple X-ray two-dimensional chest images during respiration; and determining the corresponding dynamic right lung diaphragm sequence and / or dynamic left lung diaphragm sequence based on the dynamic multiple X-ray two-dimensional chest images (dynamic two-dimensional chest images to be located during respiration / dynamic lung images during respiration); ...). The right lung apex corresponding to the right lung mask edge image sequence; the right lung diaphragm (right diaphragm) is located based on the right lung apex, the right costophrenic angle, and the right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images; and / or, the left lung apex corresponding to the left lung mask edge image sequence is determined, and the right costophrenic angle and / or the left costophrenic angle corresponding to the right lung mask edge image sequence are determined; the left lung diaphragm (left diaphragm) is located based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images.

[0017] Preferably, the method for determining the right lung apex corresponding to the right lung mask edge image sequence includes: detecting the right lung vertex corresponding to each right lung mask edge image in the right lung mask edge image sequence, and configuring the right lung vertex as the right lung apex corresponding to the right lung mask edge image sequence.

[0018] Preferably, the method for determining the left lung apex corresponding to the left lung mask edge image sequence includes: detecting the left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence, and configuring the left lung vertex as the left lung apex corresponding to the left lung mask edge image sequence.

[0019] Preferably, the method for determining the right costophrenic angle points corresponding to the right lung mask edge image sequence includes: detecting the lowest point of the right lung corresponding to each right lung mask edge image in the right lung mask edge image sequence, and configuring the lowest point of the right lung as the right costophrenic angle point corresponding to the right lung mask edge image sequence.

[0020] Preferably, the method for determining the left costophrenic angle points corresponding to the left lung mask edge image sequence includes: detecting the lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence, and configuring the lowest point of the left lung as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

[0021] Preferably, the method for locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images includes: determining corresponding first straight lines based on the right lung apex and right costophrenic angle corresponding to each of the dynamic multiple X-ray two-dimensional chest images; calculating multiple first distances from multiple first pixel positions (first pixels) on the right edge line (right heart border line closer to the heart) of the right lung mask edge image from the right lung apex to the right costophrenic angle to the first straight line; configuring the first pixel position corresponding to the largest distance among the multiple first distances as the right costophrenic angle, and configuring the mask edge line segment corresponding to the right lung mask edge image between the right costophrenic angle and the right lung mask edge image as the corresponding right lung diaphragm.

[0022] Preferably, the method for locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to the right diaphragm in each of the dynamic multi-dimensional chest X-ray images includes: determining the corresponding auxiliary point of the lung mask edge image based on the coordinate point of the right cardiophrenic angle and the set increment in the y-direction of each of the dynamic multi-dimensional chest X-ray images; determining the corresponding second straight line based on the left lung apex and the left costophrenic angle; calculating multiple second distances from multiple second pixel positions (second pixels) on the left edge line (the left heart border line closer to the heart) of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line; configuring the second pixel position point corresponding to the largest distance among the multiple second distances as the left cardiophrenic angle (left cardiophrenic angle point); and configuring the mask edge line segment of the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle point as the corresponding left diaphragm.

[0023] Preferably, before acquiring at least one mask edge image sequence of the right lung mask edge image sequence (right lung mask edge image) and the left lung mask edge image sequence (left lung mask edge image) corresponding to multiple dynamic two-dimensional chest X-ray images (two-dimensional chest X-ray images to be located), the method for determining the right lung mask edge image sequence and / or the left lung mask edge image sequence corresponding to the multiple dynamic two-dimensional chest X-ray images includes: using an erosion template of a set size to erode the right lung mask image sequence and / or the left lung mask image sequence to obtain the corresponding right lung mask erosion image sequence (right lung mask erosion image) and / or left lung mask erosion image sequence (left lung mask erosion image); and determining the right lung mask edge image sequence and / or the left lung mask edge image sequence based on the right lung mask image sequence and its corresponding right lung mask erosion image sequence and / or based on the left lung mask image sequence and its corresponding left lung mask erosion image sequence.

[0024] Preferably, the method for determining the right lung mask edge image sequence based on the right lung mask image sequence and its corresponding right lung mask erosion image sequence includes: subtracting the pixel value of the corresponding position in the right lung mask erosion image sequence from the pixel value of each position in the right lung mask image sequence to determine the right lung mask edge image sequence.

[0025] Preferably, the method for determining the left lung mask edge image sequence based on the left lung mask image sequence and its corresponding left lung mask erosion image sequence includes: subtracting the pixel value of the corresponding position in the left lung mask erosion image sequence from the pixel value of each position in the left lung mask image sequence to determine the left lung mask edge image sequence.

[0026] Preferably, before determining the right lung mask edge image sequence and / or left lung mask edge image sequence corresponding to the dynamic multiple X-ray two-dimensional chest images respectively, a preset lung field segmentation model is used to segment the lung fields of the dynamic multiple X-ray two-dimensional chest images respectively to obtain the right lung mask image sequence and / or the left lung mask image sequence.

[0027] Preferably, the method of segmenting the lung fields of the dynamic multiple X-ray two-dimensional chest images using a preset lung field segmentation model to obtain the right lung mask image sequence and / or the left lung mask image sequence includes: segmenting the lung fields of the dynamic multiple X-ray two-dimensional chest images using a preset lung field segmentation model to obtain the right lung mask image sequence (the right lung mask image to be processed) and / or the left lung mask image sequence (the left lung mask image sequence to be processed); and processing the right lung mask image sequence and / or the left lung mask image sequence to be processed using a connected component algorithm to remove over-segmented regions outside the lung fields to obtain the right lung mask image sequence and / or the left lung mask image sequence.

[0028] Preferably, the method for determining whether other right lung diaphragms are normal or abnormal right diaphragms based on the right lung baseline diaphragm length, a first length in the first length sequence excluding the right lung baseline diaphragm length, and a first preset length difference includes: calculating multiple first differences between the first length excluding the right lung baseline diaphragm length and the right lung baseline diaphragm length; if one of the multiple differences is greater than or equal to the first preset length difference, then the right diaphragm corresponding to the first difference is determined to be an abnormal right diaphragm; otherwise, it is determined to be a normal right diaphragm.

[0029] Preferably, the method for determining the left lung baseline diaphragm length by determining the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; and determining whether the other left lung diaphragms are normal or abnormal based on the left lung baseline diaphragm length, the second length in the second length sequence excluding the left lung baseline diaphragm length, and the second preset length difference, includes: calculating multiple second differences between the second length excluding the left lung baseline diaphragm length and the left lung baseline diaphragm length; if one of the multiple second differences is greater than or equal to the second preset length difference, then the left diaphragm corresponding to the second difference is determined to be an abnormal left diaphragm; otherwise, it is determined to be a normal left diaphragm.

[0030] Preferably, the method further includes: acquiring at least one diaphragm sequence corresponding to a normal right diaphragm and an abnormal right diaphragm, and a normal left diaphragm and an abnormal left diaphragm, from multiple dynamic two-dimensional chest X-ray images during respiration; wherein the diaphragm sequence includes: a right diaphragm sequence and / or a left diaphragm sequence; optimizing the location of the right cardiophrenic angle corresponding to the abnormal right diaphragm based on the right cardiophrenic angle corresponding to the normal right diaphragm; and / or optimizing the location of the left cardiophrenic angle corresponding to the abnormal left diaphragm based on the left cardiophrenic angle corresponding to the normal left diaphragm.

[0031] Preferably, the method further includes: obtaining the optimized right cardiophrenic angle corresponding to each abnormal right diaphragm; optimizing the location of each abnormal diaphragm based on the optimized right cardiophrenic angle, right costophrenic angle, and right lung mask edge image corresponding to each abnormal right diaphragm; and / or obtaining the optimized left cardiophrenic angle corresponding to each abnormal left diaphragm; optimizing the location of each abnormal left diaphragm based on the optimized left cardiophrenic angle, left costophrenic angle, and left lung mask edge image corresponding to each abnormal left diaphragm.

[0032] Preferably, the unit of the first length sequence and / or the second length sequence is configured as a pixel value; wherein the first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence; and / or, the second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

[0033] Preferably, the first preset length difference and the configured value of the first preset length difference are the same or different; and / or, the first value corresponding to the first preset length difference is configured to any value between 10 and 50 pixels; and the second value corresponding to the second preset length difference is configured to any value between 10 and 50 pixels.

[0034] According to one aspect of this disclosure, a dynamic lung image intelligent detection device is provided, comprising:

[0035] The acquisition unit is used to acquire at least one diaphragm sequence corresponding to the dynamic right lung diaphragm sequence and the dynamic left lung diaphragm sequence of multiple dynamic X-ray two-dimensional chest images during the breathing process.

[0036] The detection unit is configured to determine the baseline diaphragm length of the right lung by identifying the shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence; and based on the baseline diaphragm length of the right lung, the difference between the first length in the first length sequence excluding the baseline diaphragm length and the first preset length, determine whether the other right lung diaphragms are normal or abnormal; and / or, determine the baseline diaphragm length of the left lung by identifying the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; and based on the baseline diaphragm length of the left lung, the difference between the second length in the second length sequence excluding the baseline diaphragm length and the second preset length, determine whether the other left lung diaphragms are normal or abnormal; or,

[0037] Includes: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the aforementioned dynamic lung image intelligent detection method; or,

[0038] It includes: a computer-readable storage medium having stored thereon computer program instructions, which, when executed by a processor, implement the above-described dynamic lung image intelligent detection method.

[0039] According to one aspect of this disclosure, a computer program product is provided, including a computer program / instructions, characterized in that the computer program / instructions, when executed by a processor, implement the above-described dynamic lung image intelligent detection method.

[0040] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the above-described dynamic lung image intelligent detection method.

[0041] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement the above-described intelligent detection method for dynamic lung images.

[0042] According to one aspect of this disclosure, a medical device is provided that applies the dynamic lung image intelligent detection method as described above and / or includes the dynamic lung image intelligent detection device as described above and / or includes the computer program product as described above.

[0043] In this disclosure, a technical solution is proposed for a dynamic lung image intelligent detection method and device, program product and medical device to solve the problem that the prior art cannot detect normal or abnormal diaphragm, resulting in the inability to optimize the subsequent localization of abnormal diaphragm.

[0044] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0045] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0046] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0047] Figure 1 A flowchart illustrating a dynamic lung image intelligent detection method according to an embodiment of the present disclosure is shown.

[0048] Figure 2 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment;

[0049] Figure 3 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. Detailed Implementation

[0050] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0051] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0052] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0053] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0054] It is understood that the various method embodiments mentioned above in this disclosure can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this disclosure will not elaborate further.

[0055] In addition, this disclosure also provides a dynamic lung image intelligent detection device, electronic device, computer-readable storage medium, program and medical device, all of which can be used to implement any of the dynamic lung image intelligent detection methods provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding section on dynamic lung image intelligent detection methods, and will not be repeated here.

[0056] Figure 1 A flowchart illustrating a dynamic lung image intelligent detection method according to an embodiment of the present disclosure is shown, such as... Figure 1 As shown, the dynamic lung image intelligent detection method includes: Step S101: acquiring at least one diaphragm sequence from a dynamic right lung diaphragm sequence and a dynamic left lung diaphragm sequence corresponding to multiple dynamic X-ray two-dimensional chest images during respiration; Step S102: determining the right lung baseline diaphragm length based on the shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence; determining other right lung diaphragms as normal or abnormal right diaphragms based on the difference between the right lung baseline diaphragm length, the first length in the first length sequence excluding the right lung baseline diaphragm length, and the first preset length; and / or, determining the left lung baseline diaphragm length based on the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; determining other left lung diaphragms as normal or abnormal left diaphragms based on the difference between the left lung baseline diaphragm length, the second length in the second length sequence excluding the left lung baseline diaphragm length, and the second preset length. This addresses the problem that existing technologies cannot detect normal or abnormal diaphragms, resulting in the inability to optimize the subsequent localization of abnormal diaphragms.

[0057] In the embodiments of this disclosure and other possible embodiments, a two-dimensional chest X-ray image may be referred to as a two-dimensional chest (lung) X-ray image, a two-dimensional chest (lung) X-ray image, or a two-dimensional lung X-ray image, etc. Any combination of two or more of the terms X-ray / DR, two-dimensional, chest / lung, and image expresses the same meaning.

[0058] Step S101: Obtain at least one diaphragm sequence from the dynamic right lung diaphragm sequence and the dynamic left lung diaphragm sequence corresponding to multiple dynamic X-ray two-dimensional chest images during the breathing process.

[0059] In the embodiments of this disclosure and other possible embodiments, digital X-ray (DR) imaging equipment can provide high-resolution and real-time two-dimensional X-ray images. The DR imaging equipment can be used to image the chest to obtain corresponding two-dimensional X-ray chest images. For example, in the embodiments of this disclosure and other possible embodiments, the digital X-ray imaging equipment can be used to image the chest containing the lungs during free breathing or forced breathing to obtain multiple dynamic two-dimensional X-ray chest images (dynamic two-dimensional X-ray chest images to be located) corresponding to a continuous time series during breathing. Simultaneously, the digital X-ray imaging equipment can also be used to image the chest containing the lungs during breath-holding to obtain multiple dynamic two-dimensional X-ray chest images (two-dimensional X-ray chest images to be located) corresponding to a continuous time series during breath-holding. Specifically, the dynamic multiple two-dimensional X-ray chest images during breathing or during breath-holding include at least one two-dimensional X-ray chest image.

[0060] In the embodiments of this disclosure and other possible embodiments, the lung image to be segmented (the dynamic multiple X-ray two-dimensional chest images during the breathing process / the dynamic X-ray two-dimensional chest image to be located) is segmented into a left chest image and a right chest image; the left lung and the right lung are segmented based on the left chest image and the right chest image, respectively.

[0061] In embodiments of this disclosure and other possible embodiments, the lung image to be segmented (the dynamic multiple X-ray two-dimensional chest images during breathing / dynamic two-dimensional X-ray chest images to be located) is segmented into a left chest image and a right chest image; the left lung and right lung are segmented based on the left chest image and the right chest image, respectively; or, a preset convolutional neural network segmentation model, DR lung region label images for training the segmentation model, and multiple DR lung images to be segmented at multiple times during breathing or breath-holding (lung image to be segmented / dynamic multiple X-ray two-dimensional chest images during breathing to be segmented / dynamic two-dimensional X-ray chest images to be located) are obtained. The method for determining the DR lung region label images used to train the segmentation model includes: detecting the costal margin boundary, lung apex boundary, and mediastinal and transverse septal edges of the left and right chest images of multiple DR lung region images respectively to obtain DR lung region label images corresponding to the multiple DR lung region images; training the segmentation model using the DR lung region label images used to train the segmentation model; and, based on the trained segmentation model, segmenting the left and / or right lungs of the multiple DR lung images to be segmented (i.e., the two-dimensional chest X-ray images to be located / two-dimensional chest X-ray images), to obtain the right lung mask image and / or the left lung mask image. The lung field mask value corresponding to the right lung mask image can be configured to 1, and the lung field mask value corresponding to the left lung mask image can be configured to 2.

[0062] In the embodiments of this disclosure and other possible embodiments, the lung regions (lung fields) of multiple DR lung images (lung images to be segmented / multiple dynamic 2D chest X-ray images to be segmented during breathing / multiple dynamic 2D chest X-ray images to be segmented and located) at multiple times during breathing or breath-holding can be labeled manually to obtain the DR lung region label images (2D lung region / lung field label images) used to train the segmentation model; then, the segmentation model is trained using the DR lung region label images (2D lung region / lung field label images); finally, the trained segmentation model (preset lung field segmentation model) is used to segment the lung fields of the multiple dynamic 2D chest X-ray images to obtain the right lung mask image sequence and / or the left lung mask image sequence.

[0063] In embodiments of this disclosure and other possible embodiments, before acquiring at least one mask edge image sequence of the right lung mask edge image sequence (right lung mask edge image) and the left lung mask edge image sequence (left lung mask edge image) corresponding to multiple dynamic X-ray two-dimensional chest images (two-dimensional X-ray chest images to be located), the method for determining the right lung mask edge image sequence and / or the left lung mask edge image sequence corresponding to the multiple dynamic X-ray two-dimensional chest images includes: using an erosion template of a set size to erode the right lung mask image sequence and / or the left lung mask image sequence to obtain the corresponding right lung mask eroded image sequence (right lung mask eroded image) and / or left lung mask eroded image sequence (left lung mask eroded image); and determining the right lung mask edge image sequence and / or the left lung mask edge image sequence based on the right lung mask image sequence and its corresponding right lung mask eroded image sequence and / or based on the left lung mask image sequence and its corresponding left lung mask eroded image sequence.

[0064] In the embodiments of this disclosure and other possible embodiments, the set of left lung mask edge images corresponding to each of the dynamic multi-dimensional X-ray chest images constitutes a left lung mask edge image sequence; similarly, the set of right lung mask edge images corresponding to each of the dynamic multi-dimensional X-ray chest images constitutes a right lung mask edge image sequence.

[0065] In the embodiments of this disclosure and other possible embodiments, the set of right lung mask erosion images corresponding to each of the dynamic multi-X-ray two-dimensional chest images constitutes a right lung mask erosion image sequence; similarly, the set of left lung mask erosion images corresponding to each of the dynamic multi-X-ray two-dimensional chest images constitutes a left lung mask erosion image sequence.

[0066] In embodiments of this disclosure and other possible embodiments, the method for determining the right lung mask edge image sequence based on the right lung mask image sequence and its corresponding right lung mask erosion image sequence includes: subtracting the pixel value of the corresponding position in the right lung mask erosion image sequence from the pixel value of each position in the right lung mask image sequence to determine the right lung mask edge image sequence.

[0067] In embodiments of this disclosure and other possible embodiments, the method for determining the left lung mask edge image sequence based on the left lung mask image sequence and its corresponding left lung mask erosion image sequence includes: subtracting the pixel value of the corresponding position in the left lung mask erosion image sequence from the pixel value of each position in the left lung mask image sequence to determine the left lung mask edge image sequence.

[0068] In the embodiments of this disclosure and other possible embodiments, before determining the right lung mask edge image sequence and / or left lung mask edge image sequence corresponding to the dynamic multiple X-ray two-dimensional chest images respectively, a preset lung field segmentation model is used to segment the lung fields of the dynamic multiple X-ray two-dimensional chest images respectively to obtain the right lung mask image sequence and / or the left lung mask image sequence.

[0069] In embodiments of this disclosure and other possible embodiments, the erosion template of a set size can be configured as an N×N erosion template with a pixel value of 1. Using the N×N erosion template with a pixel value of 1, the right lung mask image and / or the left lung mask image are eroded respectively to obtain the corresponding right lung mask erosion image and / or left lung mask erosion image. Specifically, using the erosion template of a set size (N×N with a pixel value of 1), the right lung mask image and / or the left lung mask image are traversed row by row / column to obtain the corresponding right lung mask erosion image and / or left lung mask erosion image.

[0070] In embodiments of this disclosure and other possible embodiments, the erosion template of the set size can be configured as a 3×3 erosion template with a pixel value of 1 for each pixel. Each 3×3 erosion template with a pixel value of 1 traverses each lung field mask image (right lung mask image and / or left lung mask image) in rows / columns with a step size of 1 pixel to generate the corresponding right lung mask erosion image and / or left lung mask erosion image. More specifically, if the nine values ​​configured with a pixel value of 1 in the 3×3 pixel correction template are multiplied by the points of the traversed right lung mask image and / or left lung mask image, and these nine values ​​are not 0 for the first and last time, then it is considered that the lung field corresponding to the right lung mask image and / or left lung mask image has been detected. When a lung field is detected, the position information of the center of the 3×3 erosion template and its pixel value are recorded. Then, the corresponding right lung mask erosion image and / or left lung mask erosion image are generated based on the recorded position information and its pixel value (1 or 2). Next, the corresponding right lung mask erosion image and / or left lung mask erosion image are subtracted from the right lung mask image and / or left lung mask erosion image, respectively, to obtain the corresponding right lung mask edge image and / or left lung mask edge image. Here, 1 can represent the right lung and 2 can represent the left lung.

[0071] In embodiments of this disclosure and other possible embodiments, the method of segmenting the lung fields of the dynamic multiple X-ray two-dimensional chest images using a preset lung field segmentation model to obtain the right lung mask image sequence and / or the left lung mask image sequence includes: segmenting the lung fields of the dynamic multiple X-ray two-dimensional chest images using a preset lung field segmentation model to obtain the right lung mask image sequence to be processed (the right lung mask image to be processed) and / or the left lung mask image sequence to be processed (the left lung mask image sequence to be processed); and processing the right lung mask image sequence to be processed and / or the left lung mask image sequence to be processed using a connected component algorithm to remove over-segmented regions outside the lung fields to obtain the right lung mask image sequence and / or the left lung mask image sequence.

[0072] In embodiments of this disclosure and other possible embodiments, the preset convolutional neural network corresponding to the segmentation model (preset lung field segmentation model) can be configured as a Unet convolutional neural network, an nnUnet convolutional neural network, or a convolutional neural network improved based on the Unet convolutional neural network, or a convolutional neural network improved based on the nnUnet convolutional neural network. For example, the convolutional neural network improved based on the Unet convolutional neural network can be configured as a ResUnet convolutional neural network with a residual structure.

[0073] In embodiments of this disclosure and other possible embodiments, the Unet convolutional neural network or nnUnet convolutional neural network or a convolutional neural network improved based on Unet convolutional neural network or a convolutional neural network improved based on nnUnet convolutional neural network includes at least: a downsampling shrinking path, an upsampling expanding path, and a final classification layer.

[0074] In embodiments of this disclosure and other possible embodiments, before training the segmentation model using the DR lung region label image used to train the segmentation model, the DR lung region label image is data augmented to obtain an augmented DR lung region label image; and the segmentation model is trained using the augmented DR lung region label image.

[0075] In embodiments of this disclosure and other possible embodiments, the method for data augmentation of the DR lung region label image to obtain an enhanced DR lung region label image includes: performing spatial geometric transformation and / or flipping and / or rotating and / or cropping and / or scaling and / or image shifting and / or edge filling and / or random erasing and / or random occlusion operations on the DR lung region label image to obtain the enhanced DR lung region label image.

[0076] In embodiments of this disclosure and other possible embodiments, the method for data augmentation of the DR lung region label image to obtain an enhanced DR lung region label image further includes: randomly selecting any two DR lung region label images from the DR lung region label images; performing a registration operation on the arbitrary two DR lung region label images to obtain a corresponding DR lung region label registered image; and performing a fusion operation on the DR lung region label registered images to obtain the enhanced DR lung region label image. The registration operation on the arbitrary two DR lung region label images can employ existing registration algorithms or models, such as one or more of SIFT (Scale-invariant feature transform) registration algorithms or models, SURF (Speeded UpRobust Features) registration algorithms or models, ORB (Oriented FAST and Rotated BRIEF) registration algorithms or models, or other registration algorithms or models based on convolutional neural networks. For example, a registration algorithm or model based on a convolutional neural network can be configured as a registration algorithm or model based on a VGG network.

[0077] In the embodiments of this disclosure and other possible embodiments, the method of performing a fusion operation on the DR lung region label registration image to obtain an enhanced DR lung region label image includes: performing a minimum, maximum, or average operation on the pixel values ​​corresponding to the DR lung region label registration image to obtain the enhanced DR lung region label image.

[0078] In embodiments of this disclosure, a method for determining the first length sequence corresponding to the dynamic right lung diaphragm sequence includes: summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence; and obtaining the first length sequence corresponding to the dynamic right lung diaphragm sequence based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel. The method for determining the first length sequence corresponding to the dynamic right lung diaphragm sequence based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of first pixel values ​​corresponding to each right lung diaphragm by the area corresponding to each pixel to obtain the first length sequence corresponding to the dynamic right lung diaphragm sequence.

[0079] In embodiments of this disclosure, a method for determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence; and obtaining the second length sequence corresponding to the dynamic left lung diaphragm sequence based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel. The method for determining the second length sequence corresponding to the dynamic left lung diaphragm sequence based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of second pixel values ​​corresponding to each left lung diaphragm by the area corresponding to each pixel to obtain the second length sequence corresponding to the dynamic left lung diaphragm sequence.

[0080] In embodiments of this disclosure and other possible embodiments, the area corresponding to each pixel can be obtained by reading DICOM (Digital Imaging and Communications in Medicine) files corresponding to multiple dynamic two-dimensional chest X-ray images during respiration; DICOM, or Digital Imaging and Communications in Medicine, is an international standard (ISO 12052) for medical images and related information, defining a medical image format that meets clinical needs and can be used for data exchange. Alternatively, the area corresponding to each pixel can also be input via an input device (such as a keyboard).

[0081] In embodiments of this disclosure and other possible embodiments, the unit of the first length sequence and / or the second length sequence is configured as pixel values. Specifically, the first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence; and / or, the second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

[0082] Furthermore, in the embodiments disclosed herein and other possible embodiments, the first preset length difference and the numerical values ​​configured for the first preset length difference may be the same or different. For example, the first numerical value corresponding to the first preset length difference may be configured as any value between 10 and 50 pixels; the second numerical value corresponding to the second preset length difference may be configured as any value between 10 and 50 pixels. Moreover, those skilled in the art may configure other numerical values ​​for the first numerical value corresponding to the first preset length difference and the second numerical value corresponding to the second preset length difference according to actual needs.

[0083] For example, in the embodiments of this disclosure and other possible embodiments, the first value corresponding to the first preset length difference and the second value corresponding to the second preset length difference can be configured as any one of 10, 20, 30, 40, and 50, respectively.

[0084] In embodiments of this disclosure, before acquiring at least one diaphragm sequence corresponding to a dynamic right lung diaphragm sequence and a dynamic left lung diaphragm sequence from multiple dynamic two-dimensional chest X-ray images during respiration, a method for determining the corresponding dynamic right lung diaphragm sequence and / or dynamic left lung diaphragm sequence based on the multiple dynamic two-dimensional chest X-ray images during respiration includes: acquiring at least one masked edge image sequence corresponding to a right lung masked edge image sequence (right lung masked edge image) and a left lung masked edge image sequence (left lung masked edge image) from multiple dynamic two-dimensional chest X-ray images (dynamic two-dimensional chest X-ray images to be located during respiration / dynamic lung images during respiration); and determining each right lung masked edge image in the right lung masked edge image sequence. The corresponding right lung apex; the right lung diaphragm is located based on the right lung apex, the right costophrenic angle, and the right lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images (each right lung mask edge image); and / or, the left lung apex corresponding to each left lung mask edge image in the left lung mask edge image sequence is determined, and the right costophrenic angle corresponding to each left lung mask edge image in the right lung mask edge image sequence and / or the left costophrenic angle corresponding to the left lung mask edge image sequence is determined; the left lung diaphragm is located based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images (each left lung mask edge image). The set of right lung diaphragms corresponding to each of the dynamic multi-X-ray two-dimensional chest images constitutes a dynamic right lung diaphragm sequence; similarly, the set of left lung diaphragms corresponding to each of the dynamic multi-X-ray two-dimensional chest images constitutes a dynamic left lung diaphragm sequence.

[0085] In an embodiment of this disclosure, the method for determining the right lung apex corresponding to the right lung mask edge image sequence includes: detecting the right lung vertex corresponding to each right lung mask edge image in the right lung mask edge image sequence, and configuring the right lung vertex as the right lung apex corresponding to the right lung mask edge image sequence.

[0086] In an embodiment of this disclosure, the method for determining the left lung apex corresponding to the left lung mask edge image sequence includes: detecting the left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence, and configuring the left lung vertex as the left lung apex corresponding to the left lung mask edge image sequence.

[0087] In an embodiment of this disclosure, the method for determining the right costophrenic angle point corresponding to the right lung mask edge image sequence includes: detecting the lowest point of the right lung corresponding to each right lung mask edge image in the right lung mask edge image sequence, and configuring the lowest point of the right lung as the right costophrenic angle point corresponding to the right lung mask edge image sequence.

[0088] In embodiments of this disclosure, the method for determining the left costophrenic angle points corresponding to the left lung mask edge image sequence includes: detecting the lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence, and configuring the lowest point of the left lung as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

[0089] In embodiments of this disclosure, the method for locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images includes: determining corresponding first straight lines based on the right lung apex and right costophrenic angle corresponding to each of the dynamic multiple X-ray two-dimensional chest images; calculating multiple first distances from multiple first pixel positions (first pixels) on the right edge line (right heart border line near the heart) of the right lung mask edge image from the right lung apex to the right costophrenic angle to the first straight line; configuring the first pixel position corresponding to the largest distance among the multiple first distances as the right costophrenic angle, and configuring the mask edge line segment corresponding to the right lung mask edge image between the right costophrenic angle and the right lung mask edge image as the corresponding right lung diaphragm.

[0090] In embodiments of this disclosure, the method for locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to the right diaphragm in each of the dynamic multi-dimensional chest X-ray images includes: determining an auxiliary point corresponding to the lung mask edge image based on the coordinates of the right cardiophrenic angle and a set increment in the y-direction of each of the dynamic multi-dimensional chest X-ray images; determining a corresponding second straight line based on the left lung apex and the left costophrenic angle; calculating multiple second distances from multiple second pixel positions (second pixels) on the left edge line (the left heart border line closer to the heart) of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line; configuring the second pixel position corresponding to the largest distance among the multiple second distances as the left cardiophrenic angle (left cardiophrenic angle point); and configuring the mask edge line segment of the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle as the corresponding left diaphragm.

[0091] In the embodiments of this disclosure and other possible embodiments, the right costophrenic angle or left costophrenic angle corresponding to the left or right lung is close to the origin of the xoy coordinate system; the ordinate of the right or left lung apex corresponding to the left or right lung is greater than the ordinate of the corresponding right or left costophrenic angle. For example, the right costophrenic angle corresponding to the right lung is close to the origin of the xoy coordinate system, the y-axis is configured in the direction from the right costophrenic angle to the right lung apex, and the y-axis is configured in the direction from the right costophrenic angle to the left costophrenic angle.

[0092] For example, in embodiments of this disclosure and other possible embodiments, the method for determining the corresponding first straight line based on the right lung apex A1 and the right costophrenic angle B1 includes: based on the right lung apex A1(x A1 ,y A1 ) and the right costophrenic angle point B1(x B1 ,y B1 Determine the first coefficient a1, the second coefficient b1, and the third coefficient c1 corresponding to the first straight line Line1; determine the first straight line Line1 based on the first coefficient a1, the second coefficient b1, and the third coefficient c1.

[0093] Line 1: a1x+b1y+c1=0.

[0094] Furthermore, before calculating the first distances from multiple first pixels to the first straight line on the right edge line A1B1 (the right heart border line closer to the heart) of the right lung mask edge image from the right lung apex A1 to the right costophrenic angle B1, the right edge line A1B1 of the right lung mask edge image is determined based on the distance from the right lung apex A1 to the right costophrenic angle B1. The method for determining this method includes: taking the right lung apex A1 as the starting point, and along the right lung mask edge line of the right lung mask edge image, calculating the distance from the right lung apex A1 to the right costophrenic angle B1. The lengths of the first and second edge lines from the right lung apex to the right costophrenic angle B1 are calculated. The lengths of the first and second edge lines from the right lung apex A1 to the right costophrenic angle B1 are also calculated. The first and second edge lines are respectively distributed on both sides of the first straight line Line1. The longest edge line among the lengths of the first and second edge lines is configured as the right edge line A1B1 of the right lung mask edge image.

[0095] For example, in embodiments of this disclosure and other possible embodiments, calculating multiple first distances from multiple first pixels on the right edge line A1B1 (the right cardiac border line closer to the heart) of the right lung mask edge image from the right lung apex A1 to the right costophrenic angle B1 to the first straight line Line1 includes: taking the right lung apex A1 as the starting point / end point and the right costophrenic angle B1 as the end point / start point, and sequentially calculating multiple first distances from multiple first pixels to the first straight line Line1 along the right edge line A1B1 of the right lung mask edge image. Furthermore, the first pixel corresponding to the largest distance among the multiple first distances is configured as the right costophrenic angle C1, and the mask edge line segment corresponding to the right lung mask edge image between the right costophrenic angle C1 and the right costophrenic angle B1 is configured and positioned as the right diaphragm B1C1.

[0096] Specifically, in the embodiments of this disclosure and other possible embodiments, a calculation formula corresponding to the right cardiophrenic angle C1(x,y) is given.

[0097]

[0098] in, This represents the plurality of first pixel points p r1 ,p r2 ,p r3 ,...,p rn Multiple first distances (d) to the first straight line Line1 r1 (p r1 ),d r2 (p r2 ),d r3 (p r3 ),...,d rn (p rn )); r represents the right lung; n ≥ 1 and is a positive integer; max() represents taking the maximum function; (x r1 ,y r1 ),(x r2 ,y r2 ),(x r3 ,y r3 ),...,(x rn ,y rn ) represent multiple first pixel points p r1 ,p r2 ,p r3 ,...,p rn Corresponding coordinates; d r1 ,d r2 ,d r3 ,...,d rn Each represents a first pixel p. r1 ,p r2 ,pr3 ,...,p rn The corresponding Euclidean distance.

[0099] In embodiments of this disclosure, the method for locating the left diaphragm based on the right cardiophrenic angle corresponding to the right diaphragm, the left lung apex, the left costophrenic angle, and the left lung mask edge image includes: determining an auxiliary point corresponding to the lung mask edge image based on the coordinate point of the right cardiophrenic angle and a set increment in the y-direction; determining a corresponding second straight line based on the left lung apex and the left costophrenic angle; calculating multiple second distances from multiple second pixel points on the left edge line (the left heart border line closer to the heart) of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line; configuring the second pixel point corresponding to the largest distance among the multiple second distances as the left cardiophrenic angle (left cardiophrenic angle point); and configuring the mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle point as the left diaphragm.

[0100] In an embodiment of this disclosure, the method for determining the auxiliary point corresponding to the lung mask edge image based on the coordinate point of the right cardiophrenic angle and a set increment in the y-direction includes: adding the set increment in the y-direction to the ordinate of the coordinate point to obtain an auxiliary line parallel to the x-direction; and determining the intersection of the auxiliary line and the left lung mask edge image as the auxiliary point corresponding to the lung mask edge image.

[0101] In an embodiment of this disclosure, the method of determining the intersection of the auxiliary line and the left lung mask edge image as the auxiliary point corresponding to the lung mask edge image includes: the intersection of the auxiliary line and the left lung mask edge image includes: a first set of intersections and a second set of intersections; and determining the intersection with the smaller / smallest abscissa among the first set of intersections and the second set of intersections as the auxiliary point corresponding to the lung mask edge image.

[0102] For example, in embodiments of this disclosure and other possible embodiments, the ordinate y of the coordinate point C1(x,y) of the right cardiophrenic angle is... C1 Adding / subtracting the aforementioned y-direction increment Δy yields the corresponding auxiliary line (y) parallel to the x-direction. C1 + / -Δy); the auxiliary line (y C1 The intersection of the + / -Δy) line with the edge image of the left lung mask is determined as the auxiliary point C2' corresponding to the edge image of the lung mask. Specifically, the intersection of the auxiliary line with the edge image of the left lung mask includes: a first set of intersections and a second set of intersections; the intersection with the smaller / smallest x-coordinate among the first set of intersections and the second set of intersections is determined as the auxiliary point C2' corresponding to the edge image of the lung mask.

[0103] Specifically, the ordinate y of the coordinate point C1(x,y) of the right cardiophrenic angle is... C1 When adding the set increment Δy in the y-direction, the set increment Δy is configured to be negative; or, the y-coordinate of the coordinate point C1(x,y) of the right diaphragm angle is set to... C1 When subtracting the set increment Δy in the y-direction, the set increment Δy is configured to be a positive value. The calculation formula corresponding to the auxiliary point C2'(x,y) is given, namely C'2(x,y)=C1(x,y-Δy).

[0104] Further, in the embodiments of this disclosure and other possible embodiments, a corresponding second straight line Line2 is determined based on the left lung apex A2 and the left costophrenic angle B2; the method for determining the corresponding second straight line Line2 based on the left lung apex A2 and the left costophrenic angle B2 includes: based on the left lung apex A2 (x A2 ,y A2 ) and the left costophrenic angle point B2(x B2 ,y B2 Determine the fourth coefficient a2, the fifth coefficient b2, and the sixth coefficient c2 corresponding to the second line Line2; based on the fourth coefficient a2, the fifth coefficient b2, and the sixth coefficient c2, determine the second line Line2.

[0105] Line 2: a²x + b²y + c² = 0.

[0106] Furthermore, before calculating the second distances from multiple second pixels to the second straight line on the left edge line A2B2 (the left heart border line closer to the heart) of the left lung mask edge image from the left lung apex A2 to the ischiocostal angle point B2, the left edge line A2B2 of the left lung mask edge image is determined based on the left lung apex A2 to the ischiocostal angle point B2. The method for determining this method includes: taking the left lung apex A2 as the starting point, and along the left lung mask edge line of the left lung mask edge image, calculating the distances from multiple second pixels to the second straight line. The lengths of the third and fourth edge lines from the left lung apex A2 to the left costophrenic angle B2 are calculated. The third and fourth edge lines are located on opposite sides of the second straight line Line2. The longest edge line between the lengths of the first and second edge lines is designated as the left edge line A2B2 of the right lung mask edge image.

[0107] For example, in embodiments of this disclosure and other possible embodiments, calculating multiple second distances from multiple second pixels on the left edge line A1B1 (the left cardiac border line closer to the heart) of the right lung mask edge image from the left lung apex A2 to the ischiocostal angle B2 to the second straight line includes: taking the left lung apex A2 as the starting / ending point and the ischiocostal angle B2 as the ending / starting point, and sequentially calculating multiple second distances from multiple second pixels to the second straight line Line2 along the left edge line A1B1 of the left lung mask edge image. Furthermore, the second pixel corresponding to the largest distance among the multiple second distances is configured as the right cardiodiaphragm angle C2, and the mask edge line segment corresponding to the right lung mask edge image between the left cardiodiaphragm angle C2 and the ischiocostal angle B2 is configured and positioned as the left diaphragm B2C2.

[0108] Specifically, in the embodiments of this disclosure and other possible embodiments, a calculation formula for the right cardiophrenic angle C2(x,y) is given.

[0109]

[0110] in, This represents the plurality of second pixel points p l1 ,p l2 ,p l3 ,...,p ln Multiple first distances d to the second straight line Line2 l1 (p l1 ),d l2 (p l2 ),d l3 (p l3 ),...,d ln (p ln ); l represents the right lung; n ≥ 1 and is a positive integer; max() represents taking the maximum function; (x l1 ,y l1 ),(x l2 ,y l2 ),(x l3 ,y l3 ),...,(x ln ,y ln ) represent multiple first pixel points p l1 ,p l2 ,p l3 ,...,p ln Corresponding coordinates; d l1 ,d l2 ,d l3 ,...,d ln Each represents a first pixel p. l1 ,p l2 ,pl3 ,...,p ln The corresponding Euclidean distance.

[0111] Step S102: Determine the baseline diaphragm length of the right lung by the shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence; based on the baseline diaphragm length of the right lung, the difference between the first length in the first length sequence excluding the baseline diaphragm length of the right lung and the first preset length, determine whether the other right lung diaphragms are normal or abnormal right diaphragms; and / or, determine the baseline diaphragm length of the left lung by the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; based on the baseline diaphragm length of the left lung, the difference between the second length in the second length sequence excluding the baseline diaphragm length of the left lung and the second preset length, determine whether the other left lung diaphragms are normal or abnormal left diaphragms.

[0112] In embodiments of this disclosure, the method of determining the right lung baseline diaphragm length by the shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence, and determining the other right lung diaphragms as normal or abnormal based on the right lung baseline diaphragm length, the first length in the first length sequence excluding the right lung baseline diaphragm length, and the difference between the first and first preset lengths, includes: determining the right lung baseline diaphragm by the shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence; and determining the other right lung diaphragms as normal or abnormal based on the first baseline length corresponding to the right lung baseline diaphragm, the first length in the dynamic right lung diaphragm sequence excluding the right lung baseline diaphragm, and the difference between the first and first preset lengths, respectively.

[0113] In embodiments of this disclosure, the method of determining the left lung baseline diaphragm length by the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence, and determining the other left lung diaphragms as normal or abnormal based on the difference between the left lung baseline diaphragm length, the second length in the second length sequence excluding the left lung baseline diaphragm length, and the second preset length, includes: determining the left lung baseline diaphragm by the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; and determining the other left lung diaphragms as normal or abnormal based on the difference between the second baseline length corresponding to the left lung baseline diaphragm, the second length in the dynamic left lung diaphragm sequence excluding the left lung baseline diaphragm, and the second preset length, respectively.

[0114] In embodiments of this disclosure, the method for determining whether other right lung diaphragms are normal or abnormal right diaphragms based on the right lung baseline diaphragm length, a first length in the first length sequence excluding the right lung baseline diaphragm length, and a first preset length difference includes: calculating multiple first differences between the first length excluding the right lung baseline diaphragm length and the right lung baseline diaphragm length; if one of the multiple differences is greater than or equal to the first preset length difference, then the right diaphragm corresponding to the first difference is determined to be an abnormal right diaphragm; otherwise, it is determined to be a normal right diaphragm.

[0115] For example, in embodiments of this disclosure and other possible embodiments, the first preset length difference is configured as 20 mm or 20 pixels. Multiple first differences are calculated between a first length other than the reference diaphragm length of the right lung and the reference diaphragm length of the right lung. If one of the multiple differences is greater than or equal to the first preset length difference of 20 mm or 20 pixels, the right diaphragm corresponding to that first difference is identified as an abnormal right diaphragm; if one of the multiple differences is less than the first preset length difference of 20 mm or 20 pixels, the right diaphragm corresponding to that first difference is identified as a normal right diaphragm.

[0116] In embodiments of this disclosure, the method of determining the left lung baseline diaphragm length by the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; and determining whether the other left lung diaphragms are normal or abnormal based on the left lung baseline diaphragm length, the second length in the second length sequence excluding the left lung baseline diaphragm length, and the second preset length difference, includes: calculating multiple second differences between the second length excluding the left lung baseline diaphragm length and the left lung baseline diaphragm length; if one of the multiple second differences is greater than or equal to the second preset length difference, then the left diaphragm corresponding to the second difference is determined to be an abnormal left diaphragm; otherwise, it is determined to be a normal left diaphragm.

[0117] For example, in embodiments of this disclosure and other possible embodiments, the first preset length difference is configured as 20 mm or 20 pixels. Multiple second differences are calculated between a second length (excluding the reference diaphragm length for the left lung) and the reference diaphragm length for the left lung. If one of the multiple second differences is greater than or equal to the second preset length difference of 20 mm or 20 pixels, the left diaphragm corresponding to that second difference is identified as an abnormal left diaphragm. If one of the multiple second differences is less than the second preset length difference of 20 mm or 20 pixels, and if one of the multiple second differences is greater than or equal to the second preset length difference of 20 mm or 20 pixels, it indicates a normal left diaphragm.

[0118] In embodiments of this disclosure, the dynamic lung image intelligent detection method further includes: acquiring at least one diaphragm sequence corresponding to a normal right diaphragm and an abnormal right diaphragm, and a normal left diaphragm and an abnormal left diaphragm, from multiple dynamic two-dimensional chest X-ray images during respiration; wherein the diaphragm sequence includes: a right diaphragm sequence and / or a left diaphragm sequence; optimizing the location of the right cardiophrenic angle corresponding to the abnormal right diaphragm based on the right cardiophrenic angle corresponding to the normal right diaphragm; and / or optimizing the location of the left cardiophrenic angle corresponding to the abnormal left diaphragm based on the left cardiophrenic angle corresponding to the normal left diaphragm.

[0119] In embodiments of this disclosure and other possible embodiments, the method for optimizing the localization of the right cardiophrenic angle corresponding to the abnormal right diaphragm based on the right cardiophrenic angle corresponding to the normal right diaphragm includes: acquiring a normal right diaphragm sequence, an abnormal right diaphragm sequence, a normal left diaphragm sequence, and an abnormal left diaphragm sequence corresponding to multiple dynamic X-ray two-dimensional chest images during respiration; using the normal right cardiophrenic angle corresponding to the normal right diaphragm in the normal right diaphragm sequence to optimize the localization of the abnormal left cardiophrenic angle corresponding to the abnormal left diaphragm in the abnormal left diaphragm sequence of the same first X-ray two-dimensional chest image; and / or, using the normal left cardiophrenic angle corresponding to the normal left diaphragm in the normal left diaphragm sequence to optimize the localization of the abnormal right cardiophrenic angle corresponding to the abnormal right diaphragm in the abnormal right diaphragm sequence of the same second X-ray two-dimensional chest image.

[0120] In the embodiments of this disclosure and other possible embodiments, before optimizing the localization of the abnormal left cardiophrenic angle corresponding to the abnormal left diaphragm in the abnormal left diaphragm sequence of the same first X-ray two-dimensional chest image using the normal right cardiophrenic angle corresponding to the normal right diaphragm in the normal right diaphragm sequence, the right diaphragm corresponding to the same first X-ray two-dimensional chest image is a normal right diaphragm. Similarly, before optimizing the localization of the abnormal right cardiophrenic angle corresponding to the abnormal right diaphragm in the abnormal right diaphragm sequence of the same second X-ray two-dimensional chest image using the normal left cardiophrenic angle corresponding to the normal left diaphragm in the normal left diaphragm sequence, the left diaphragm corresponding to the same second X-ray two-dimensional chest image is a normal left diaphragm.

[0121] In embodiments of this disclosure and other possible embodiments, the method for optimizing the localization of the abnormal left cardiophrenic angle corresponding to the abnormal left diaphragm in the abnormal left diaphragm sequence of the same first X-ray two-dimensional chest image using the normal right cardiophrenic angle corresponding to the normal right diaphragm in the normal right diaphragm sequence includes: determining standard X-ray two-dimensional chest images corresponding to the normal right diaphragm and normal left diaphragm in the dynamic multi-image X-ray two-dimensional chest images; configuring the normal left cardiophrenic angle corresponding to the normal left diaphragm in the standard X-ray two-dimensional chest image to the initial left cardiophrenic angle corresponding to the abnormal left diaphragm in the abnormal left diaphragm sequence of the same first X-ray two-dimensional chest image; calculating a first distance in the y-direction between the normal left cardiophrenic angle corresponding to the normal left diaphragm in the standard X-ray two-dimensional chest image and the normal left cardiophrenic angle corresponding to the normal left diaphragm in the first X-ray two-dimensional chest image; adjusting the initial left cardiophrenic angle using the first distance in the y-direction to obtain an adjusted left cardiophrenic angle; and optimizing the localization of the abnormal left cardiophrenic angle based on the adjusted left cardiophrenic angle and the left lung mask edge image corresponding to the first X-ray two-dimensional chest image.

[0122] In embodiments of this disclosure and other possible embodiments, the method of adjusting the initial left diaphragm angle using the first distance in the y-direction to obtain the adjusted left diaphragm angle includes: adjusting the y-direction coordinate point of the initial left diaphragm angle using the first distance in the y-direction to obtain the adjusted left diaphragm angle; wherein, the method of adjusting the y-direction coordinate point of the initial left diaphragm angle using the first distance in the y-direction to obtain the adjusted left diaphragm angle includes: if the first distance in the y-direction is greater than or equal to 0, then adding the first distance in the y-direction to the initial left diaphragm angle's y-direction coordinate point to obtain the adjusted left diaphragm angle; wherein, the x-direction coordinate point corresponding to the adjusted left diaphragm angle remains unchanged.

[0123] In embodiments of this disclosure and other possible embodiments, the method for optimizing the location of the abnormal left cardiophrenic angle based on the adjusted left cardiophrenic angle and the left lung mask edge image corresponding to the first two-dimensional chest X-ray image includes: configuring the intersection point of the first straight line parallel to the x-direction corresponding to the adjusted left cardiophrenic angle and the left lung mask edge image corresponding to the first two-dimensional chest X-ray image as the optimized left cardiophrenic angle; wherein, the method for configuring the focal point of the first straight line parallel to the x-direction corresponding to the adjusted left cardiophrenic angle and the left lung mask edge image corresponding to the first two-dimensional chest X-ray image as the optimized left cardiophrenic angle includes: drawing a first straight line parallel to the x-direction using the y-direction coordinate point corresponding to the adjusted left cardiophrenic angle; configuring the intersection point with the smallest x-direction coordinate point among the intersection points of the first straight line and the left lung mask edge image corresponding to the first two-dimensional chest X-ray image as the optimized left cardiophrenic angle.

[0124] In embodiments of this disclosure and other possible embodiments, the method for determining the standard two-dimensional chest images corresponding to the normal right diaphragm and normal left diaphragm in the dynamic multi-image X-ray two-dimensional chest images includes: acquiring a first moment corresponding to the first two-dimensional chest image; and determining the X-ray two-dimensional chest image corresponding to the normal right diaphragm and normal left diaphragm in the dynamic multi-image X-ray two-dimensional chest images closest to the first moment as the standard two-dimensional chest image.

[0125] In embodiments of this disclosure and other possible embodiments, the method for optimizing the localization of the abnormal right cardiophrenic angle corresponding to the abnormal right diaphragm in the abnormal right diaphragm sequence of the same second X-ray two-dimensional chest image using the normal left cardiophrenic angle corresponding to the normal left diaphragm in the normal left diaphragm sequence includes: determining standard X-ray two-dimensional chest images corresponding to the normal left diaphragm and normal right diaphragm in the dynamic multi-image X-ray two-dimensional chest images; configuring the normal right cardiophrenic angle corresponding to the normal right diaphragm in the standard X-ray two-dimensional chest image to the initial right cardiophrenic angle corresponding to the abnormal right diaphragm in the abnormal right diaphragm sequence of the same second X-ray two-dimensional chest image; calculating a second distance in the y-direction between the normal right cardiophrenic angle corresponding to the normal right diaphragm in the standard X-ray two-dimensional chest image and the normal right cardiophrenic angle corresponding to the normal right diaphragm in the second X-ray two-dimensional chest image; adjusting the initial right cardiophrenic angle using the second distance in the y-direction to obtain an adjusted right cardiophrenic angle; and optimizing the localization of the abnormal right cardiophrenic angle based on the adjusted right cardiophrenic angle and the right lung mask edge image corresponding to the second X-ray two-dimensional chest image.

[0126] In embodiments of this disclosure and other possible embodiments, the method of adjusting the initial right diaphragm angle using the second distance in the y-direction to obtain the adjusted right diaphragm angle includes: adjusting the y-direction coordinate point of the initial right diaphragm angle using the second distance in the y-direction to obtain the adjusted right diaphragm angle; wherein, the method of adjusting the y-direction coordinate point of the initial right diaphragm angle using the second distance in the y-direction to obtain the adjusted right diaphragm angle includes: if the second distance in the y-direction is greater than or equal to 0, then adding the second distance in the y-direction to the initial right diaphragm angle to obtain the adjusted right diaphragm angle; wherein, the x-direction coordinate point corresponding to the adjusted right diaphragm angle remains unchanged.

[0127] In embodiments of this disclosure and other possible embodiments, the method for optimizing the localization of the abnormal right cardiophrenic angle based on the adjusted right cardiophrenic angle and the right lung mask edge image corresponding to the second two-dimensional chest X-ray image includes: configuring the intersection point of the second straight line parallel to the x-direction corresponding to the adjusted right cardiophrenic angle and the right lung mask edge image corresponding to the second two-dimensional chest X-ray image as the localized optimized right cardiophrenic angle; wherein, the method for configuring the focal point of the second straight line parallel to the x-direction corresponding to the adjusted right cardiophrenic angle and the right lung mask edge image corresponding to the second two-dimensional chest X-ray image as the localized optimized right cardiophrenic angle includes: drawing a second straight line parallel to the x-direction using the y-direction coordinate point corresponding to the adjusted right cardiophrenic angle; configuring the intersection point with the largest x-direction coordinate point among the intersection points of the second straight line and the right lung mask edge in the right lung mask edge image corresponding to the second two-dimensional chest X-ray image as the localized optimized right cardiophrenic angle.

[0128] In embodiments of this disclosure and other possible embodiments, the method for determining the standard two-dimensional chest images corresponding to the normal left diaphragm and normal right diaphragm in the dynamic multi-image two-dimensional chest X-ray images includes: acquiring a second time corresponding to the second two-dimensional chest image; and determining the two-dimensional chest image corresponding to the normal right diaphragm and normal left diaphragm in the dynamic multi-image two-dimensional chest X-ray images closest to the second time as the standard two-dimensional chest image.

[0129] In embodiments of this disclosure, the dynamic lung image intelligent detection method further includes: acquiring the optimized right cardiophrenic angle corresponding to each abnormal right diaphragm; optimizing the location of each abnormal diaphragm based on the optimized right cardiophrenic angle, right costophrenic angle, and right lung mask edge image corresponding to each abnormal right diaphragm; and / or acquiring the optimized left cardiophrenic angle corresponding to each abnormal left diaphragm; optimizing the location of each abnormal left diaphragm based on the optimized left cardiophrenic angle, left costophrenic angle, and left lung mask edge image corresponding to each abnormal left diaphragm.

[0130] In the embodiments of this disclosure and other possible embodiments, a method for optimizing the localization of each abnormal right diaphragm based on the localized optimized right cardiophrenic angle, right costophrenic angle point, and right lung mask edge image corresponding to each abnormal right diaphragm includes: configuring the line segment between the localized optimized right cardiophrenic angle and the right costophrenic angle point corresponding to each abnormal right diaphragm and the right lung mask edge in the right lung mask edge image as the localized optimized right diaphragm.

[0131] Similarly, in the embodiments of this disclosure and other possible embodiments, the method for optimizing the localization of each abnormal left diaphragm based on the localized optimized left cardiophrenic angle, left costophrenic angle point and left lung mask edge image corresponding to each abnormal left diaphragm includes: configuring the line segment between the localized optimized left cardiophrenic angle and the left costophrenic angle point corresponding to each abnormal left diaphragm and the left lung mask edge in the left lung mask edge image as the localized optimized left diaphragm.

[0132] The executing entity of the dynamic lung image intelligent detection method can be an image processing device. For example, the dynamic lung image intelligent detection method can be executed by a terminal device, a server, or other processing devices. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, in-vehicle device, wearable device, etc. In some possible implementations, the dynamic lung image intelligent detection method can be implemented by a processor calling computer-readable instructions stored in memory.

[0133] Those skilled in the art will understand that in the above-described intelligent detection method for dynamic lung images in specific embodiments, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0134] This disclosure also proposes a dynamic lung image intelligent detection device, comprising: an acquisition unit, configured to acquire at least one diaphragm sequence corresponding to a dynamic right lung diaphragm sequence and a dynamic left lung diaphragm sequence from multiple dynamic X-ray two-dimensional chest images during respiration; a detection unit, configured to determine the right lung baseline diaphragm length based on the shortest length in a first length sequence corresponding to the dynamic right lung diaphragm sequence; and, based on the right lung baseline diaphragm length, a first length in the first length sequence excluding the right lung baseline diaphragm length, and a first preset length difference, to determine other right lung diaphragms as normal right diaphragms or abnormal right diaphragms; and / or, to determine the left lung baseline diaphragm length based on the shortest length in a second length sequence corresponding to the dynamic left lung diaphragm sequence; and, based on the left lung baseline diaphragm length, a second length in the second length sequence excluding the left lung baseline diaphragm length, and a second preset length difference, to determine other left lung diaphragms as normal left diaphragms or abnormal left diaphragms.

[0135] In embodiments of this disclosure and other possible embodiments, the dynamic lung image intelligent detection device further includes: a cardiophrenic angle localization optimization unit, used to acquire at least one diaphragm sequence corresponding to a normal right diaphragm and an abnormal right diaphragm, and a normal left diaphragm and an abnormal left diaphragm sequence corresponding to a dynamic multi-X-ray two-dimensional chest image during respiration; wherein, the diaphragm sequence includes: a right diaphragm sequence and / or a left diaphragm sequence; the localization optimization of the right cardiophrenic angle corresponding to the abnormal right diaphragm is performed based on the right cardiophrenic angle corresponding to the normal right diaphragm; and / or, the localization optimization of the left cardiophrenic angle corresponding to the abnormal left diaphragm is performed based on the left cardiophrenic angle corresponding to the normal left diaphragm.

[0136] In embodiments of this disclosure and other possible embodiments, the dynamic lung image intelligent detection device further includes: a diaphragm localization optimization unit, configured to acquire the localized optimized right cardiophrenic angle corresponding to each abnormal right diaphragm; perform localization optimization on each abnormal diaphragm based on the localized optimized right cardiophrenic angle, right costophrenic angle point, and right lung mask edge image corresponding to each abnormal right diaphragm; and / or acquire the localized optimized left cardiophrenic angle corresponding to each abnormal left diaphragm; perform localization optimization on each abnormal diaphragm based on the localized optimized left cardiophrenic angle, left costophrenic angle point, and left lung mask edge image corresponding to each abnormal left diaphragm.

[0137] In embodiments of this disclosure and other possible embodiments, the detection unit includes at least one of a right diaphragm detection unit and a left diaphragm detection unit; wherein, the right diaphragm detection unit is used to determine the right lung baseline diaphragm length based on the shortest length in the first length sequence corresponding to the dynamic right lung diaphragm sequence; and to determine the other right lung diaphragms as normal right diaphragms or abnormal right diaphragms based on the difference between the right lung baseline diaphragm length, the first length in the first length sequence excluding the right lung baseline diaphragm length, and the first preset length; the left diaphragm detection unit is used to determine the left lung baseline diaphragm length based on the shortest length in the second length sequence corresponding to the dynamic left lung diaphragm sequence; and to determine the other left lung diaphragms as normal left diaphragms or abnormal left diaphragms based on the difference between the left lung baseline diaphragm length, the second length in the second length sequence excluding the left lung baseline diaphragm length, and the second preset length.

[0138] In embodiments of this disclosure and other possible embodiments, the right diaphragm detection unit includes: a right lung baseline diaphragm length determination unit and a first comparison unit; wherein, the right lung baseline diaphragm length determination unit is used to determine the right lung baseline diaphragm as the shortest right lung diaphragm in the first length sequence corresponding to the dynamic right lung diaphragm sequence; the first comparison unit is used to determine, based on the first baseline length corresponding to the right lung baseline diaphragm, the first length corresponding to other right lung diaphragms in the dynamic right lung diaphragm sequence excluding the right lung baseline diaphragm, and the first preset length difference, respectively, whether the other right lung diaphragms are normal right diaphragms or abnormal right diaphragms.

[0139] In embodiments of this disclosure and other possible embodiments, the left diaphragm detection unit includes: a left lung baseline diaphragm length determination unit and a second comparison unit; wherein, the left lung baseline diaphragm length determination unit is used to determine the left lung baseline diaphragm as the shortest left lung diaphragm in the second length sequence corresponding to the dynamic left lung diaphragm sequence; the second comparison unit is used to determine, based on the second baseline length corresponding to the left lung baseline diaphragm, the second length corresponding to other left lung diaphragms in the dynamic left lung diaphragm sequence excluding the left lung baseline diaphragm, and the difference between the second and second preset lengths, the other left lung diaphragms as normal left diaphragms or abnormal left diaphragms.

[0140] In embodiments of this disclosure and other possible embodiments, one or more of a dynamic right lung diaphragm sequence determination unit and a dynamic left lung diaphragm sequence determination unit are further included. The dynamic right lung diaphragm sequence determination unit is used to acquire a right lung mask edge image sequence corresponding to multiple dynamic two-dimensional chest X-ray images; determine the right lung apex corresponding to the right lung mask edge image sequence; and locate the right lung diaphragm based on the right lung apex, the right costophrenic angle, and the right lung mask edge image corresponding to each of the multiple dynamic two-dimensional chest X-ray images. The dynamic left lung diaphragm sequence determination unit is used to acquire a left lung mask edge image sequence corresponding to multiple dynamic two-dimensional chest X-ray images; determine the left lung apex corresponding to the left lung mask edge image sequence; and determine the right costophrenic angle and / or the left costophrenic angle corresponding to the right lung mask edge image sequence and / or the left costophrenic angle corresponding to the left lung mask edge image sequence; and locate the left lung diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to the right lung diaphragm in each of the multiple dynamic two-dimensional chest X-ray images.

[0141] In embodiments of this disclosure and other possible embodiments, the right diaphragm detection unit further includes one or more of a first length sequence determination unit and a second length sequence determination unit; wherein, the first length sequence determination unit is used to respectively count the sum of the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence; and respectively obtain a first length sequence corresponding to the dynamic right lung diaphragm sequence based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel. The second length sequence determination unit is used to respectively count the sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence; and respectively obtain a second length sequence corresponding to the dynamic left lung diaphragm sequence based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

[0142] Further, in the embodiments of this disclosure and other possible embodiments, the first length sequence determination unit includes: a first multiplication unit; the first multiplication unit is used to multiply the sum of the number of first pixel values ​​corresponding to each right lung diaphragm by the area corresponding to each pixel to obtain the first length sequence corresponding to the dynamic right lung diaphragm sequence.

[0143] Furthermore, in the embodiments of this disclosure and other possible embodiments, the second length sequence determination unit includes: a second multiplication unit; the second multiplication unit is used to multiply the sum of the number of second pixel values ​​corresponding to each left lung diaphragm by the area corresponding to each pixel to obtain the second length sequence corresponding to the dynamic left lung diaphragm sequence.

[0144] This disclosure also proposes a dynamic lung image intelligent detection device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the above-described dynamic lung image intelligent detection method; wherein the above-described dynamic lung image intelligent detection method includes at least the following steps: acquiring at least one diaphragm sequence of a dynamic right lung diaphragm sequence and a dynamic left lung diaphragm sequence corresponding to multiple dynamic X-ray two-dimensional chest images during respiration; determining the shortest length of the first length sequence corresponding to the dynamic right lung diaphragm sequence as the right lung baseline diaphragm length; determining the other right lung diaphragms as normal right diaphragms or abnormal right diaphragms based on the right lung baseline diaphragm length, a first length in the first length sequence excluding the right lung baseline diaphragm length, and a first preset length difference; and / or determining the shortest length of the second length sequence corresponding to the dynamic left lung diaphragm sequence as the left lung baseline diaphragm length; determining the other left lung diaphragms as normal left diaphragms or abnormal left diaphragms based on the left lung baseline diaphragm length, a second length in the second length sequence excluding the left lung baseline diaphragm length, and a second preset length difference.

[0145] This disclosure also proposes a dynamic lung image intelligent detection device, comprising: a computer-readable storage medium storing computer program instructions thereon, wherein the computer program instructions, when executed by a processor, implement the aforementioned dynamic lung image intelligent detection method; wherein the aforementioned dynamic lung image intelligent detection method comprises at least the following steps: acquiring at least one diaphragm sequence corresponding to a dynamic right lung diaphragm sequence and a dynamic left lung diaphragm sequence from multiple dynamic X-ray two-dimensional chest images during respiration; determining the right lung baseline diaphragm length based on the shortest length in a first length sequence corresponding to the dynamic right lung diaphragm sequence; determining other right lung diaphragms as normal right diaphragms or abnormal right diaphragms based on the difference between the right lung baseline diaphragm length, a first length in the first length sequence excluding the right lung baseline diaphragm length, and a first preset length; and / or determining the left lung baseline diaphragm length based on the shortest length in a second length sequence corresponding to the dynamic left lung diaphragm sequence; determining other left lung diaphragms as normal left diaphragms or abnormal left diaphragms based on the difference between the left lung baseline diaphragm length, a second length in the second length sequence excluding the left lung baseline diaphragm length, and a second preset length.

[0146] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to execute the dynamic lung image intelligent detection method described in the above method embodiments. The specific implementation can be referred to the description of the dynamic lung image intelligent detection method embodiments above, and for the sake of brevity, it will not be repeated here.

[0147] This disclosure also proposes a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the aforementioned intelligent detection method for dynamic lung images. The intelligent detection method for dynamic lung images includes at least the following steps: acquiring at least one diaphragm sequence from a dynamic right lung diaphragm sequence and a dynamic left lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during respiration; determining the shortest length in a first length sequence corresponding to the dynamic right lung diaphragm sequence as the right lung baseline diaphragm length; determining other right lung diaphragms as normal or abnormal right diaphragms based on the right lung baseline diaphragm length, a first length in the first length sequence excluding the right lung baseline diaphragm length, and a first preset length difference; and / or determining the shortest length in a second length sequence corresponding to the dynamic left lung diaphragm sequence as the left lung baseline diaphragm length; and determining other left lung diaphragms as normal or abnormal left diaphragms based on the left lung baseline diaphragm length, a second length in the second length sequence excluding the left lung baseline diaphragm length, and a second preset length difference. Computer-readable storage media can be non-volatile computer-readable storage media.

[0148] This disclosure also proposes an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured for the aforementioned intelligent detection method for dynamic lung images; wherein the aforementioned intelligent detection method for dynamic lung images includes at least the following steps: acquiring at least one diaphragm sequence corresponding to a dynamic right lung diaphragm sequence and a dynamic left lung diaphragm sequence from multiple dynamic two-dimensional chest X-ray images during respiration; determining the shortest length in a first length sequence corresponding to the dynamic right lung diaphragm sequence as the right lung baseline diaphragm length; determining other right lung diaphragms as normal right diaphragms or abnormal right diaphragms based on the right lung baseline diaphragm length, a first length in the first length sequence excluding the right lung baseline diaphragm length, and a first preset length difference; and / or determining the shortest length in a second length sequence corresponding to the dynamic left lung diaphragm sequence as the left lung baseline diaphragm length; determining other left lung diaphragms as normal left diaphragms or abnormal left diaphragms based on the left lung baseline diaphragm length, a second length in the second length sequence excluding the left lung baseline diaphragm length, and a second preset length difference. Among them, electronic devices can be provided as terminals, servers, or other forms of devices.

[0149] This disclosure also proposes a computer program product, including a computer program / instruction, which, when executed by a processor, implements the aforementioned intelligent detection method for dynamic lung images. The intelligent detection method for dynamic lung images includes at least the following steps: acquiring at least one diaphragm sequence from a dynamic right lung diaphragm sequence and a dynamic left lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during respiration; determining the shortest length in a first length sequence corresponding to the dynamic right lung diaphragm sequence as the right lung baseline diaphragm length; determining other right lung diaphragms as normal or abnormal right diaphragms based on the right lung baseline diaphragm length, a first length in the first length sequence excluding the right lung baseline diaphragm length, and a first preset length difference; and / or determining the shortest length in a second length sequence corresponding to the dynamic left lung diaphragm sequence as the left lung baseline diaphragm length; determining other left lung diaphragms as normal or abnormal left diaphragms based on the left lung baseline diaphragm length, a second length in the second length sequence excluding the left lung baseline diaphragm length, and a second preset length difference.

[0150] This disclosure also proposes a dynamic lung image intelligent detection system or medical device, which applies the dynamic lung image intelligent detection method as described above and / or includes the dynamic lung image intelligent detection device as described above and / or includes the computer program product as described above.

[0151] Figure 2This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, or other terminal.

[0152] Reference Figure 2 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0153] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0154] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0155] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0156] Multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). Meanwhile, audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, recording mode, or voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0157] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0158] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0159] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0160] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0161] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 804 including computer program instructions that can be executed by a processor 820 of an electronic device 800 to perform the above-described method.

[0162] Figure 3 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. For example, the electronic device 1900 may be provided as a server. (Refer to...) Figure 3 The electronic device 1900 includes a processing component 1922, which further includes one or more processors, and memory resources represented by memory 1932 for storing instructions, such as application programs, that can be executed by the processing component 1922. The application programs stored in memory 1932 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 1922 is configured to execute instructions to perform the methods described above.

[0163] Electronic device 1900 may also include a power supply component 1926 configured to perform power management of electronic device 1900, a wired or wireless network interface 1950 configured to connect electronic device 1900 to a network, and an input / output (I / O) interface 1958. Electronic device 1900 can operate on an operating system stored in memory 1932, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, or similar.

[0164] In an exemplary embodiment, a non-volatile computer-readable storage medium is also provided, such as a memory 1932 including computer program instructions that can be executed by a processing component 1922 of an electronic device 1900 to perform the above-described method.

[0165] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0166] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A dynamic lung image intelligent detection method, characterized in that, include: Acquire dynamic right lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during respiration; Before acquiring the dynamic right lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during the breathing process, the method includes: acquiring right lung mask edge image sequences corresponding to multiple dynamic two-dimensional chest X-ray images; determining the right lung apex corresponding to the right lung mask edge image sequence; and locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the multiple dynamic two-dimensional chest X-ray images. The shortest right lung diaphragm in the first length sequence corresponding to the dynamic right lung diaphragm sequence is determined as the reference length of the right lung diaphragm. Based on the right lung baseline diaphragm length, the first length in the first length sequence excluding the right lung baseline diaphragm length, and the first preset length difference, other right lung diaphragms are determined to be normal or abnormal right diaphragms, including: calculating multiple first differences between the first length in the first length sequence excluding the right lung baseline diaphragm length and the right lung baseline diaphragm length; if one of the multiple first differences is greater than or equal to the first preset length difference, the right diaphragm corresponding to the first difference is determined to be an abnormal right diaphragm; otherwise, it is determined to be a normal right diaphragm.

2. The intelligent detection method for dynamic lung images according to claim 1, characterized in that, Determining the first length sequence corresponding to the dynamic right lung diaphragm sequence includes: The sum of the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence is counted respectively; The first length sequence corresponding to the dynamic right lung diaphragm sequence is obtained based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel.

3. The intelligent detection method for dynamic lung images according to claim 2, characterized in that, The step of determining the first length sequence corresponding to the dynamic right lung diaphragm sequence based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of first pixel values ​​corresponding to each right lung diaphragm by the area corresponding to each pixel to obtain the first length sequence corresponding to the dynamic right lung diaphragm sequence.

4. The intelligent detection method for dynamic lung images according to any one of claims 1-3, characterized in that, The step of determining the right lung apex corresponding to the right lung mask edge image sequence includes: The right lung vertex corresponding to each right lung mask edge image in the right lung mask edge image sequence is detected respectively; The right lung apex is configured as the right lung tip corresponding to the right lung mask edge image sequence.

5. The intelligent detection method for dynamic lung images according to any one of claims 1-3, characterized in that, Determining the right costophrenic angle points corresponding to the right lung mask edge image sequence, including: The lowest point of the right lung corresponding to each right lung mask edge image in the right lung mask edge image sequence is detected respectively; The lowest point of the right lung is configured as the right costophrenic angle point corresponding to the right lung mask edge image sequence.

6. The intelligent detection method for dynamic lung images according to claim 4, characterized in that, Determining the right costophrenic angle points corresponding to the right lung mask edge image sequence, including: The lowest point of the right lung corresponding to each right lung mask edge image in the right lung mask edge image sequence is detected respectively; The lowest point of the right lung is configured as the right costophrenic angle point corresponding to the right lung mask edge image sequence.

7. The intelligent detection method for dynamic lung images according to any one of claims 1-3 and 6, characterized in that, The step of locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images includes: Based on the right lung apex and right costophrenic angle points corresponding to the respective dynamic multiple X-ray two-dimensional chest images, the corresponding first straight line is determined; Calculate the first distances from multiple first pixel locations on the right edge line of the masked edge image of the right lung, from the right lung apex to the right costophrenic angle, to the first straight line; Configure the first pixel position point corresponding to the largest distance among the plurality of first distances as the right diaphragm angle; The mask edge line segments corresponding to the mask edge images of the right lung between the right cardiophrenic angle and the right costophrenic angle are respectively configured and positioned as the corresponding right lung diaphragm.

8. The intelligent detection method for dynamic lung images according to claim 4, characterized in that, The step of locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images includes: Based on the right lung apex and right costophrenic angle points corresponding to the respective dynamic multiple X-ray two-dimensional chest images, the corresponding first straight line is determined; Calculate the first distances from multiple first pixel locations on the right edge line of the masked edge image of the right lung, from the right lung apex to the right costophrenic angle, to the first straight line; Configure the first pixel position point corresponding to the largest distance among the plurality of first distances as the right diaphragm angle; The mask edge line segments corresponding to the mask edge images of the right lung between the right cardiophrenic angle and the right costophrenic angle are respectively configured and positioned as the corresponding right lung diaphragm.

9. The intelligent detection method for dynamic lung images according to claim 5, characterized in that, The step of locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images includes: Based on the right lung apex and right costophrenic angle points corresponding to the respective dynamic multiple X-ray two-dimensional chest images, the corresponding first straight line is determined; Calculate the first distances from multiple first pixel locations on the right edge line of the masked edge image of the right lung, from the right lung apex to the right costophrenic angle, to the first straight line; Configure the first pixel position point corresponding to the largest distance among the plurality of first distances as the right diaphragm angle; The mask edge line segments corresponding to the mask edge images of the right lung between the right cardiophrenic angle and the right costophrenic angle are respectively configured and positioned as the corresponding right lung diaphragm.

10. The intelligent detection method for dynamic lung images according to any one of claims 1-3, 6, 8, and 9, characterized in that, Also includes: The right diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, corresponding to multiple dynamic two-dimensional chest X-ray images during respiration. The location of the right cardiophrenic angle corresponding to the abnormal right diaphragm is optimized based on the right cardiophrenic angle corresponding to the normal right diaphragm.

11. The intelligent detection method for dynamic lung images according to claim 4, characterized in that, Also includes: The right diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, corresponding to multiple dynamic two-dimensional chest X-ray images during respiration. The location of the right cardiophrenic angle corresponding to the abnormal right diaphragm is optimized based on the right cardiophrenic angle corresponding to the normal right diaphragm.

12. The intelligent detection method for dynamic lung images according to claim 5, characterized in that, Also includes: The right diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, corresponding to multiple dynamic two-dimensional chest X-ray images during respiration. The location of the right cardiophrenic angle corresponding to the abnormal right diaphragm is optimized based on the right cardiophrenic angle corresponding to the normal right diaphragm.

13. The intelligent detection method for dynamic lung images according to claim 7, characterized in that, Also includes: The right diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, corresponding to multiple dynamic two-dimensional chest X-ray images during respiration. The location of the right cardiophrenic angle corresponding to the abnormal right diaphragm is optimized based on the right cardiophrenic angle corresponding to the normal right diaphragm.

14. The intelligent detection method for dynamic lung images according to claim 10, characterized in that, Also includes: Obtain the optimized right cardiophrenic angle corresponding to each of the aforementioned abnormal right diaphragms; Based on the optimized right cardiophrenic angle, right costophrenic angle, and right lung mask edge image corresponding to each abnormal right diaphragm, the localization of each abnormal right diaphragm is optimized.

15. The intelligent detection method for dynamic lung images according to any one of claims 11-14, characterized in that, Also includes: Obtain the optimized right cardiophrenic angle corresponding to each of the aforementioned abnormal right diaphragms; Based on the optimized right cardiophrenic angle, right costophrenic angle, and right lung mask edge image corresponding to each abnormal right diaphragm, the localization of each abnormal right diaphragm is optimized.

16. The intelligent detection method for dynamic lung images according to any one of claims 1-3, 6, 8, 9, and 11-14, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

17. The intelligent detection method for dynamic lung images according to claim 4, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

18. The intelligent detection method for dynamic lung images according to claim 5, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

19. The intelligent detection method for dynamic lung images according to claim 7, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

20. The intelligent detection method for dynamic lung images according to claim 10, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

21. The intelligent detection method for dynamic lung images according to claim 15, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

22. The intelligent detection method for dynamic lung images according to any one of claims 1-3, 6, 8, 9, 11-14, and 17-21, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

23. The intelligent detection method for dynamic lung images according to claim 4, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

24. The intelligent detection method for dynamic lung images according to claim 5, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

25. The intelligent detection method for dynamic lung images according to claim 7, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

26. The intelligent detection method for dynamic lung images according to claim 10, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

27. The intelligent detection method for dynamic lung images according to claim 15, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

28. The intelligent detection method for dynamic lung images according to claim 16, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

29. A dynamic lung image intelligent detection method, characterized in that, include: Acquire dynamic left lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during respiration; Before acquiring the dynamic left lung diaphragm sequence corresponding to multiple dynamic X-ray two-dimensional chest images during respiration, the method includes: acquiring at least one masked edge image sequence of the right lung masked edge image sequence and the left lung masked edge image sequence corresponding to the multiple dynamic X-ray two-dimensional chest images; determining the left lung apex corresponding to the left lung masked edge image sequence; determining the right costophrenic angle point corresponding to the right lung masked edge image sequence and / or the left costophrenic angle point corresponding to the left lung masked edge image sequence; and locating the left lung diaphragm based on the right cardiophrenic angle corresponding to the right lung diaphragm in each of the multiple dynamic X-ray two-dimensional chest images, the left lung apex, the left costophrenic angle point, and the left lung masked edge image. The shortest left lung diaphragm in the second length sequence corresponding to the dynamic left lung diaphragm sequence is determined as the left lung baseline diaphragm length. Based on the left lung baseline diaphragm length, the second length in the second length sequence excluding the left lung baseline diaphragm length, and the second preset length difference, other left lung diaphragms are determined to be normal or abnormal left diaphragms, including: calculating multiple second differences between the second length excluding the left lung baseline diaphragm length and the left lung baseline diaphragm length; if one of the multiple second differences is greater than or equal to the second preset length difference, the left diaphragm corresponding to that second difference is determined to be an abnormal left diaphragm; otherwise, it is determined to be a normal left diaphragm.

30. The intelligent detection method for dynamic lung images according to claim 29, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

31. The intelligent detection method for dynamic lung images according to claim 30, characterized in that, The step of determining the second length sequence corresponding to the dynamic left lung diaphragm sequence based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of second pixel values ​​corresponding to each left lung diaphragm by the area corresponding to each pixel to obtain the second length sequence corresponding to the dynamic left lung diaphragm sequence.

32. The intelligent detection method for dynamic lung images according to any one of claims 29-31, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

33. The intelligent detection method for dynamic lung images according to any one of claims 29-31, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

34. The intelligent detection method for dynamic lung images according to claim 32, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

35. The intelligent detection method for dynamic lung images according to any one of claims 29-31 and 34, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle corresponding to the right diaphragm in each of the dynamic multi-X-ray two-dimensional chest images, the left lung apex, the left costophrenic angle, and the left lung mask edge image includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

36. The intelligent detection method for dynamic lung images according to claim 32, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle corresponding to the right diaphragm in each of the dynamic multi-X-ray two-dimensional chest images, the left lung apex, the left costophrenic angle, and the left lung mask edge image includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

37. The intelligent detection method for dynamic lung images according to claim 33, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle corresponding to the right diaphragm in each of the dynamic multi-X-ray two-dimensional chest images, the left lung apex, the left costophrenic angle, and the left lung mask edge image includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

38. The intelligent detection method for dynamic lung images according to any one of claims 29-31, 34, 36, and 37, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

39. The intelligent detection method for dynamic lung images according to claim 32, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

40. The intelligent detection method for dynamic lung images according to claim 33, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

41. The intelligent detection method for dynamic lung images according to claim 35, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

42. The intelligent detection method for dynamic lung images according to claim 38, characterized in that, Also includes: Obtain the optimized left cardiophrenic angle corresponding to each of the abnormal left diaphragms; Based on the optimized left cardiophrenic angle, left costophrenic angle, and left lung mask edge image corresponding to each abnormal left diaphragm, the localization of each abnormal diaphragm is optimized.

43. The intelligent detection method for dynamic lung images according to any one of claims 39-41, characterized in that, Also includes: Obtain the optimized left cardiophrenic angle corresponding to each of the abnormal left diaphragms; Based on the optimized left cardiophrenic angle, left costophrenic angle, and left lung mask edge image corresponding to each abnormal left diaphragm, the localization of each abnormal diaphragm is optimized.

44. The intelligent detection method for dynamic lung images according to any one of claims 29-31, 34, 36, 37, and 39-42, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

45. The intelligent detection method for dynamic lung images according to claim 32, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

46. ​​The intelligent detection method for dynamic lung images according to claim 33, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

47. The intelligent detection method for dynamic lung images according to claim 35, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

48. The intelligent detection method for dynamic lung images according to claim 38, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

49. The intelligent detection method for dynamic lung images according to claim 43, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

50. The intelligent detection method for dynamic lung images according to any one of claims 29-31, 34, 36, 37, 39-42, and 45-49, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

51. The intelligent detection method for dynamic lung images according to claim 32, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

52. The intelligent detection method for dynamic lung images according to claim 33, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

53. The intelligent detection method for dynamic lung images according to claim 35, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

54. The intelligent detection method for dynamic lung images according to claim 38, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

55. The intelligent detection method for dynamic lung images according to claim 43, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

56. The intelligent detection method for dynamic lung images according to claim 44, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

57. A dynamic lung image intelligent detection method, characterized in that, include: Acquire at least one diaphragm sequence corresponding to dynamic right lung diaphragm sequence and dynamic left lung diaphragm sequence of multiple dynamic X-ray two-dimensional chest images during respiration. Before acquiring at least one diaphragm sequence corresponding to the dynamic right lung diaphragm sequence and the dynamic left lung diaphragm sequence of multiple dynamic X-ray two-dimensional chest images during the respiratory process, the method includes: acquiring at least one masked edge image sequence corresponding to the right lung masked edge image sequence and the left lung masked edge image sequence of the multiple dynamic X-ray two-dimensional chest images; determining the right lung apex corresponding to the right lung masked edge image sequence; locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung masked edge image corresponding to each of the multiple dynamic X-ray two-dimensional chest images; determining the left lung apex corresponding to the left lung masked edge image sequence; determining the right costophrenic angle and / or the left costophrenic angle corresponding to the right lung masked edge image sequence and / or the left costophrenic angle corresponding to the left lung masked edge image sequence; locating the left lung diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung masked edge image corresponding to the right lung diaphragm of the multiple dynamic X-ray two-dimensional chest images. The shortest right lung diaphragm in the first length sequence corresponding to the dynamic right lung diaphragm sequence is determined as the reference length of the right lung diaphragm. Based on the right lung baseline diaphragm length, the first length in the first length sequence excluding the right lung baseline diaphragm length, and the first preset length difference, other right lung diaphragms are determined to be normal or abnormal right diaphragms, including: calculating multiple first differences between the first length in the first length sequence excluding the right lung baseline diaphragm length and the right lung baseline diaphragm length; if one of the multiple first differences is greater than or equal to the first preset length difference, the right diaphragm corresponding to the first difference is determined to be an abnormal right diaphragm; otherwise, it is determined to be a normal right diaphragm. The shortest left lung diaphragm in the second length sequence corresponding to the dynamic left lung diaphragm sequence is determined as the left lung baseline diaphragm length. Based on the left lung baseline diaphragm length, the second length in the second length sequence excluding the left lung baseline diaphragm length, and the second preset length difference, other left lung diaphragms are determined to be normal or abnormal left diaphragms, including: calculating multiple second differences between the second length excluding the left lung baseline diaphragm length and the left lung baseline diaphragm length; if one of the multiple second differences is greater than or equal to the second preset length difference, the left diaphragm corresponding to that second difference is determined to be an abnormal left diaphragm; otherwise, it is determined to be a normal left diaphragm.

58. The intelligent detection method for dynamic lung images according to claim 57, characterized in that, Determining the first length sequence corresponding to the dynamic right lung diaphragm sequence includes: The sum of the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence is counted respectively; The first length sequence corresponding to the dynamic right lung diaphragm sequence is obtained based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel.

59. The intelligent detection method for dynamic lung images according to claim 58, characterized in that, The step of determining the first length sequence corresponding to the dynamic right lung diaphragm sequence based on the sum of the number of first pixel values ​​corresponding to each right lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of first pixel values ​​corresponding to each right lung diaphragm by the area corresponding to each pixel to obtain the first length sequence corresponding to the dynamic right lung diaphragm sequence.

60. The intelligent detection method for dynamic lung images according to any one of claims 57-59, characterized in that, The step of determining the right lung apex corresponding to the right lung mask edge image sequence includes: The right lung vertex corresponding to each right lung mask edge image in the right lung mask edge image sequence is detected respectively; The right lung apex is configured as the right lung tip corresponding to the right lung mask edge image sequence.

61. The intelligent detection method for dynamic lung images according to any one of claims 57-59, characterized in that, Determining the right costophrenic angle points corresponding to the right lung mask edge image sequence, including: The lowest point of the right lung corresponding to each right lung mask edge image in the right lung mask edge image sequence is detected respectively; The lowest point of the right lung is configured as the right costophrenic angle point corresponding to the right lung mask edge image sequence.

62. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, Determining the right costophrenic angle points corresponding to the right lung mask edge image sequence, including: The lowest point of the right lung corresponding to each right lung mask edge image in the right lung mask edge image sequence is detected respectively; The lowest point of the right lung is configured as the right costophrenic angle point corresponding to the right lung mask edge image sequence.

63. The intelligent detection method for dynamic lung images according to any one of claims 57-59 and 62, characterized in that, The step of locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images includes: Based on the right lung apex and right costophrenic angle points corresponding to the respective dynamic multiple X-ray two-dimensional chest images, the corresponding first straight line is determined; Calculate the first distances from multiple first pixel locations on the right edge line of the masked edge image of the right lung, from the right lung apex to the right costophrenic angle, to the first straight line; Configure the first pixel position point corresponding to the largest distance among the plurality of first distances as the right diaphragm angle; The mask edge line segments corresponding to the mask edge images of the right lung between the right cardiophrenic angle and the right costophrenic angle are respectively configured and positioned as the corresponding right lung diaphragm.

64. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, The step of locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images includes: Based on the right lung apex and right costophrenic angle points corresponding to the respective dynamic multiple X-ray two-dimensional chest images, the corresponding first straight line is determined; Calculate the first distances from multiple first pixel locations on the right edge line of the masked edge image of the right lung, from the right lung apex to the right costophrenic angle, to the first straight line; Configure the first pixel position point corresponding to the largest distance among the plurality of first distances as the right diaphragm angle; The mask edge line segments corresponding to the mask edge images of the right lung between the right cardiophrenic angle and the right costophrenic angle are respectively configured and positioned as the corresponding right lung diaphragm.

65. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, The step of locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multiple X-ray two-dimensional chest images includes: Based on the right lung apex and right costophrenic angle points corresponding to the respective dynamic multiple X-ray two-dimensional chest images, the corresponding first straight line is determined; Calculate the first distances from multiple first pixel locations on the right edge line of the masked edge image of the right lung, from the right lung apex to the right costophrenic angle, to the first straight line; Configure the first pixel position point corresponding to the largest distance among the plurality of first distances as the right diaphragm angle; The mask edge line segments corresponding to the mask edge images of the right lung between the right cardiophrenic angle and the right costophrenic angle are respectively configured and positioned as the corresponding right lung diaphragm.

66. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, and 65, characterized in that, Also includes: The right diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, corresponding to multiple dynamic two-dimensional chest X-ray images during respiration. The location of the right cardiophrenic angle corresponding to the abnormal right diaphragm is optimized based on the right cardiophrenic angle corresponding to the normal right diaphragm.

67. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, Also includes: The right diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, corresponding to multiple dynamic two-dimensional chest X-ray images during respiration. The location of the right cardiophrenic angle corresponding to the abnormal right diaphragm is optimized based on the right cardiophrenic angle corresponding to the normal right diaphragm.

68. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, Also includes: The right diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, corresponding to multiple dynamic two-dimensional chest X-ray images during respiration. The location of the right cardiophrenic angle corresponding to the abnormal right diaphragm is optimized based on the right cardiophrenic angle corresponding to the normal right diaphragm.

69. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, Also includes: The right diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, corresponding to multiple dynamic two-dimensional chest X-ray images during respiration. The location of the right cardiophrenic angle corresponding to the abnormal right diaphragm is optimized based on the right cardiophrenic angle corresponding to the normal right diaphragm.

70. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, Also includes: Obtain the optimized right cardiophrenic angle corresponding to each of the aforementioned abnormal right diaphragms; Based on the optimized right cardiophrenic angle, right costophrenic angle, and right lung mask edge image corresponding to each abnormal right diaphragm, the localization of each abnormal right diaphragm is optimized.

71. The intelligent detection method for dynamic lung images according to any one of claims 67-70, characterized in that, Also includes: Obtain the optimized right cardiophrenic angle corresponding to each of the aforementioned abnormal right diaphragms; Based on the optimized right cardiophrenic angle, right costophrenic angle, and right lung mask edge image corresponding to each abnormal right diaphragm, the localization of each abnormal right diaphragm is optimized.

72. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, and 67-70, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

73. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

74. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

75. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

76. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

77. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, Also includes: Configure the unit of the first length sequence as pixel value; The first length sequence is obtained by summing the number of first pixel values ​​corresponding to each right lung diaphragm in the dynamic right lung diaphragm sequence.

78. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, and 73-77, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

79. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

80. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

81. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

82. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

83. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

84. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, The first value corresponding to the first preset length difference is configured to be any value between 10 and 50 pixels.

85. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, 73-77, and 79-84, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

86. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

87. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

88. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

89. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

90. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

91. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

92. The intelligent detection method for dynamic lung images according to claim 78, characterized in that, Determining the second length sequence corresponding to the dynamic left lung diaphragm sequence includes: The sum of the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence is counted respectively; The second length sequence corresponding to the dynamic left lung diaphragm sequence is obtained based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel.

93. The intelligent detection method for dynamic lung images according to claim 85, characterized in that, The step of determining the second length sequence corresponding to the dynamic left lung diaphragm sequence based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of second pixel values ​​corresponding to each left lung diaphragm by the area corresponding to each pixel to obtain the second length sequence corresponding to the dynamic left lung diaphragm sequence.

94. The intelligent detection method for dynamic lung images according to any one of claims 86-92, characterized in that, The step of determining the second length sequence corresponding to the dynamic left lung diaphragm sequence based on the sum of the number of second pixel values ​​corresponding to each left lung diaphragm and the area corresponding to each pixel includes: multiplying the sum of the number of second pixel values ​​corresponding to each left lung diaphragm by the area corresponding to each pixel to obtain the second length sequence corresponding to the dynamic left lung diaphragm sequence.

95. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, 73-77, 79-84, and 86-93, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

96. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

97. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

98. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

99. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

100. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

101. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

102. The intelligent detection method for dynamic lung images according to claim 78, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

103. The intelligent detection method for dynamic lung images according to claim 85, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

104. The intelligent detection method for dynamic lung images according to claim 94, characterized in that, The step of determining the left lung apex corresponding to the left lung mask edge image sequence includes: The left lung vertex corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; the left lung vertex is configured as the left lung tip corresponding to the left lung mask edge image sequence respectively.

105. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, 73-77, 79-84, 86-93, and 96-104, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

106. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

107. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

108. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

109. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

110. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

111. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

112. The intelligent detection method for dynamic lung images according to claim 78, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

113. The intelligent detection method for dynamic lung images according to claim 85, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

114. The intelligent detection method for dynamic lung images according to claim 94, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

115. The intelligent detection method for dynamic lung images according to claim 95, characterized in that, Determine the left costophrenic angle points corresponding to the left lung mask edge image sequence, including: The lowest point of the left lung corresponding to each left lung mask edge image in the left lung mask edge image sequence is detected respectively; The lowest point of the left lung is configured as the left costophrenic angle point corresponding to the left lung mask edge image sequence.

116. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, 73-77, 79-84, 86-93, 96-104, and 106-115, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

117. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

118. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

119. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

120. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

121. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

122. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

123. The intelligent detection method for dynamic lung images according to claim 78, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

124. The intelligent detection method for dynamic lung images according to claim 85, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

125. The intelligent detection method for dynamic lung images according to claim 94, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

126. The intelligent detection method for dynamic lung images according to claim 95, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

127. The intelligent detection method for dynamic lung images according to claim 105, characterized in that, The method of locating the left diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle, and the left lung mask edge image corresponding to each of the dynamic multi-X-ray two-dimensional chest images includes: Based on the coordinates of the right cardiophrenic angle corresponding to each of the dynamic multi-X-ray two-dimensional chest images and the set increment in the y-direction, the auxiliary points corresponding to the lung mask edge images are determined. Based on the left lung apex and the left costophrenic angle, a corresponding second straight line is determined; multiple second distances from multiple second pixel positions on the left edge line of the left lung mask edge image from the right lung apex to the auxiliary point to the second straight line are calculated respectively. Configure the second pixel position point corresponding to the largest distance among the plurality of second distances as the left diaphragm angle; The mask edge line segment corresponding to the left lung mask edge image between the left cardiophrenic angle and the left costophrenic angle is configured and positioned as the corresponding left lung diaphragm.

128. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, 73-77, 79-84, 86-93, 96-104, 106-115, and 117-127, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

129. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

130. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

131. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

132. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

133. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

134. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

135. The intelligent detection method for dynamic lung images according to claim 78, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

136. The intelligent detection method for dynamic lung images according to claim 85, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

137. The intelligent detection method for dynamic lung images according to claim 94, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

138. The intelligent detection method for dynamic lung images according to claim 95, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

139. The intelligent detection method for dynamic lung images according to claim 105, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

140. The intelligent detection method for dynamic lung images according to claim 116, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

141. The intelligent detection method for dynamic lung images according to claim 128, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

142. The intelligent detection method for dynamic lung images according to any one of claims 129-141, characterized in that, Also includes: The left diaphragm sequence is obtained from at least one diaphragm sequence corresponding to the normal right diaphragm and the abnormal right diaphragm, and the normal left diaphragm and the abnormal left diaphragm, based on multiple dynamic two-dimensional chest X-ray images during respiration. The left cardiophrenic angle corresponding to the abnormal left diaphragm is located and optimized based on the left cardiophrenic angle corresponding to the normal left diaphragm.

143. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, 73-77, 79-84, 86-93, 96-104, 106-115, 117-127, and 129-141, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

144. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

145. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

146. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

147. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

148. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

149. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

150. The intelligent detection method for dynamic lung images according to claim 78, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

151. The intelligent detection method for dynamic lung images according to claim 85, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

152. The intelligent detection method for dynamic lung images according to claim 94, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

153. The intelligent detection method for dynamic lung images according to claim 95, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

154. The intelligent detection method for dynamic lung images according to claim 105, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

155. The intelligent detection method for dynamic lung images according to claim 116, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

156. The intelligent detection method for dynamic lung images according to claim 128, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

157. The intelligent detection method for dynamic lung images according to claim 142, characterized in that, Also includes: Configure the unit of the second length sequence as pixel value; The second length sequence is obtained by summing the number of second pixel values ​​corresponding to each left lung diaphragm in the dynamic left lung diaphragm sequence.

158. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, 73-77, 79-84, 86-93, 96-104, 106-115, 117-127, 129-141, and 144-157, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

159. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

160. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

161. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

162. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

163. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

164. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

165. The intelligent detection method for dynamic lung images according to claim 78, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

166. The intelligent detection method for dynamic lung images according to claim 85, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

167. The intelligent detection method for dynamic lung images according to claim 94, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

168. The intelligent detection method for dynamic lung images according to claim 95, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

169. The intelligent detection method for dynamic lung images according to claim 105, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

170. The intelligent detection method for dynamic lung images according to claim 116, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

171. The intelligent detection method for dynamic lung images according to claim 128, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

172. The intelligent detection method for dynamic lung images according to claim 142, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

173. The intelligent detection method for dynamic lung images according to claim 143, characterized in that, The second value corresponding to the second preset length difference is configured to be any value between 10 and 50 pixels.

174. The intelligent detection method for dynamic lung images according to any one of claims 57-59, 62, 64, 65, 67-70, 73-77, 79-84, 86-92, 96-104, 106-115, 117-127, 129-141, 144-157, and 159-173, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

175. The intelligent detection method for dynamic lung images according to claim 60, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

176. The intelligent detection method for dynamic lung images according to claim 61, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

177. The intelligent detection method for dynamic lung images according to claim 63, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

178. The intelligent detection method for dynamic lung images according to claim 66, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

179. The intelligent detection method for dynamic lung images according to claim 71, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

180. The intelligent detection method for dynamic lung images according to claim 72, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

181. The intelligent detection method for dynamic lung images according to claim 78, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

182. The intelligent detection method for dynamic lung images according to claim 85, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

183. The intelligent detection method for dynamic lung images according to claim 94, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

184. The intelligent detection method for dynamic lung images according to claim 95, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

185. The intelligent detection method for dynamic lung images according to claim 105, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

186. The intelligent detection method for dynamic lung images according to claim 116, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

187. The intelligent detection method for dynamic lung images according to claim 128, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

188. The intelligent detection method for dynamic lung images according to claim 142, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

189. The intelligent detection method for dynamic lung images according to claim 143, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

190. The intelligent detection method for dynamic lung images according to claim 158, characterized in that, The first preset length difference and the second preset length difference may have the same or different values.

191. A dynamic lung image intelligent detection device, characterized in that, include: The acquisition unit is used to acquire the dynamic right lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during the respiratory process. Before acquiring the dynamic right lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during the breathing process, the method includes: acquiring right lung mask edge image sequences corresponding to multiple dynamic two-dimensional chest X-ray images; determining the right lung apex corresponding to the right lung mask edge image sequence; and locating the right lung diaphragm based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the multiple dynamic two-dimensional chest X-ray images. The detection unit is used to determine the shortest right lung diaphragm in the first length sequence corresponding to the dynamic right lung diaphragm sequence as the right lung baseline diaphragm length; based on the right lung baseline diaphragm length, the first length in the first length sequence excluding the right lung baseline diaphragm length, and the first preset length difference, to determine other right lung diaphragms as normal or abnormal right diaphragms, including: calculating multiple first differences between the first length in the first length sequence excluding the right lung baseline diaphragm length and the right lung baseline diaphragm length; if one of the multiple first differences is greater than or equal to the first preset length difference, then the right diaphragm corresponding to the first difference is determined as an abnormal right diaphragm; otherwise, it is determined as a normal right diaphragm.

192. A dynamic lung image intelligent detection device, characterized in that, include: The acquisition unit is used to acquire the dynamic left lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during the respiratory process. Before acquiring the dynamic left lung diaphragm sequence corresponding to multiple dynamic X-ray two-dimensional chest images during respiration, the method includes: acquiring at least one masked edge image sequence of the right lung masked edge image sequence and the left lung masked edge image sequence corresponding to the multiple dynamic X-ray two-dimensional chest images; determining the left lung apex corresponding to the left lung masked edge image sequence; determining the right costophrenic angle point corresponding to the right lung masked edge image sequence and / or the left costophrenic angle point corresponding to the left lung masked edge image sequence; and locating the left lung diaphragm based on the right cardiophrenic angle, the left lung apex, the left costophrenic angle point, and the left lung masked edge image corresponding to the right lung diaphragm in each of the multiple dynamic X-ray two-dimensional chest images. The detection unit is used to determine the shortest left lung diaphragm in the second length sequence corresponding to the dynamic left lung diaphragm sequence as the left lung baseline diaphragm length; based on the left lung baseline diaphragm length, the second length in the second length sequence other than the left lung baseline diaphragm length, and the second preset length difference, to determine other left lung diaphragms as normal or abnormal left diaphragms, including: calculating multiple second differences between the second length other than the left lung baseline diaphragm length and the left lung baseline diaphragm length; if one of the multiple second differences is greater than or equal to the second preset length difference, then the left diaphragm corresponding to the second difference is determined as an abnormal left diaphragm; otherwise, it is determined as a normal left diaphragm.

193. A dynamic lung image intelligent detection device, characterized in that, include: The acquisition unit is used to acquire at least one diaphragm sequence of dynamic right lung diaphragm sequence and dynamic left lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during respiration; acquiring at least one diaphragm sequence of dynamic right lung diaphragm sequence and dynamic left lung diaphragm sequence corresponding to multiple dynamic two-dimensional chest X-ray images during respiration includes: acquiring at least one masked edge image sequence of right lung masked edge image sequence and left lung masked edge image sequence corresponding to multiple dynamic two-dimensional chest X-ray images; determining the right lung apex corresponding to the right lung masked edge image sequence; and further... The right lung diaphragm is located based on the right lung apex, right costophrenic angle, and right lung mask edge image corresponding to each of the dynamic multi-dimensional chest X-ray images; the left lung apex is determined according to the left lung mask edge image sequence; the right costophrenic angle and / or the left costophrenic angle corresponding to the right lung mask edge image sequence are determined according to the left lung mask edge image sequence; the left lung diaphragm is located based on the right cardiophrenic angle, left lung apex, left costophrenic angle, and left lung mask edge image corresponding to each of the dynamic multi-dimensional chest X-ray images; The detection unit is used to determine the shortest right lung diaphragm in the first length sequence corresponding to the dynamic right lung diaphragm sequence as the right lung baseline diaphragm length; based on the right lung baseline diaphragm length, the first length in the first length sequence excluding the right lung baseline diaphragm length, and the first preset length difference, to determine other right lung diaphragms as normal or abnormal right diaphragms, including: calculating multiple first differences between the first length in the first length sequence excluding the right lung baseline diaphragm length and the right lung baseline diaphragm length; if one of the multiple first differences is greater than or equal to the first preset length difference, then the right diaphragm corresponding to that first difference is determined as an abnormal right diaphragm; otherwise, it is determined that... The left diaphragm is defined as normal; the shortest left diaphragm in the second length sequence corresponding to the dynamic left diaphragm sequence is defined as the left diaphragm baseline length; based on the left diaphragm baseline length, the second length in the second length sequence excluding the left diaphragm baseline length, and the second preset length difference, other left diaphragms are respectively determined as normal or abnormal left diaphragms, including: calculating multiple second differences between the second length excluding the left diaphragm baseline length and the left diaphragm baseline length; if one of the multiple second differences is greater than or equal to the second preset length difference, the left diaphragm corresponding to the second difference is determined as an abnormal left diaphragm; otherwise, it is determined as a normal left diaphragm.

194. A dynamic lung image intelligent detection device, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the dynamic lung image intelligent detection method according to any one of claims 1 to 190.

195. A dynamic lung image intelligent detection device, characterized in that, include: A computer-readable storage medium having stored thereon computer program instructions that, when executed by a processor, implement the dynamic lung image intelligent detection method according to any one of claims 1 to 190.

196. A computer program product, comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the dynamic lung image intelligent detection method according to any one of claims 1 to 190.

197. A medical device, characterized in that, The method for intelligent detection of dynamic lung images as described in any one of claims 1 to 190 is applied.

198. A medical device, characterized in that, Includes the dynamic lung image intelligent detection device as described in any one of claims 191 to 195.

199. A medical device, characterized in that, Including the computer program product as described in claim 196.

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