Lung field area determination, lung development assessment method and apparatus, electronic device, and medium

Abstract

This disclosure relates to a method and apparatus, electronic device, and storage medium for determining lung field area and assessing lung development, and pertains to the field of DR image processing technology. Specifically, the method for determining lung field area includes: acquiring two-dimensional DR images of the left lung and / or the right lung at multiple time points during respiration; and determining the area of ​​the left lung and / or the right lung during respiration based on the two-dimensional DR images of the left lung and / or the right lung at the same time points. Embodiments of this disclosure can achieve lung field area determination and lung development assessment.

Description

Technical Field

[0001] This disclosure relates to the field of quantitative analysis technology of DR images, and in particular to a method and device for determining lung field area and assessing lung development, as well as electronic devices and storage media. Background Technology

[0002] Digital X-ray (DR) imaging can provide high-resolution and real-time X-ray images and has been widely used in examinations of the skeletal system, chest, and dentistry, such as fracture diagnosis, lung disease screening, and dental X-rays.

[0003] During respiration, inhalation requires a decrease in intrapulmonary pressure. The intrapulmonary pressure first drops below atmospheric pressure, allowing air to enter the lungs from outside. Then, during inhalation, the intrapulmonary pressure gradually increases until it matches atmospheric pressure, at which point inhalation ceases, preparing for exhalation. As adults age, the size of the thoracic cavity gradually decreases, limiting lung capacity and altering the elasticity of muscles that aid in respiration. Furthermore, after birth, lung tissue continues to develop and grow until maturity, with the alveoli and small blood vessels in the lungs multiplying, and lung volume increasing. Therefore, changes in lung area during respiration are one of the most important imaging parameters reflecting the respiratory process. Based on this, it is necessary to determine the lung field area during respiration, and then use this lung field area for lung disease diagnosis or developmental evaluation. Summary of the Invention

[0004] This disclosure presents a method and device, electronic equipment, and storage medium for determining lung field area and assessing lung development in DR images.

[0005] According to one aspect of this disclosure, a method for determining the lung field area in a DR image is provided, comprising:

[0006] Acquire two-dimensional DR images of the left lung and / or two-dimensional DR images of the right lung at multiple time points during respiration;

[0007] Based on two-dimensional DR images of the left lung and / or two-dimensional DR images of the right lung at multiple moments during the breathing process, the area of ​​the left lung and / or the area of ​​the right lung during the breathing process are determined.

[0008] Preferably, the method for determining the area of ​​the left lung and / or the area of ​​the right lung during the breathing process based on two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple time points during the breathing process includes:

[0009] Get the set area corresponding to a single pixel;

[0010] The number of pixels in the left lung in the two-dimensional DR left lung image and / or the number of pixels in the right lung in the two-dimensional DR right lung image at multiple time points during the breathing process are counted respectively.

[0011] Based on the set area, calculate the left lung area and / or right lung area corresponding to the number of left lung pixels in the two-dimensional DR left lung image and / or the number of right lung pixels in the two-dimensional DR right lung image at multiple moments during the breathing process.

[0012] Preferably, the method for calculating the left lung area and / or right lung area corresponding to the number of left lung pixels in the two-dimensional DR left lung image and / or the number of right lung pixels in the two-dimensional DR right lung image at multiple time points during the breathing process, based on the set area, includes:

[0013] The left lung area during the breathing process is obtained by multiplying the set area by the number of left lung pixels in the two-dimensional DR left lung images at multiple moments during the breathing process; and / or, the right lung area during the breathing process is obtained by multiplying the set area by the number of right lung pixels in the two-dimensional DR right lung images at multiple moments during the breathing process.

[0014] Preferably, before acquiring the two-dimensional DR left lung image and / or two-dimensional DR right lung image at multiple moments during the breathing process, the two-dimensional DR lung image at multiple moments during the breathing process is acquired, and the two-dimensional DR lung image at multiple moments during the breathing process is segmented into left and right lungs to obtain the two-dimensional DR left lung image and two-dimensional DR right lung image at multiple moments.

[0015] Preferably, the method for segmenting the left and right lungs of the two-dimensional DR lung images at multiple time points during the breathing process to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points includes: detecting the costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edges of the left and right chest images of the two-dimensional DR lung images at multiple time points during the breathing process, respectively, to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points; or,

[0016] Obtain a pre-defined convolutional neural network segmentation model and DR lung region label images used to train the segmentation model; train the segmentation model using the DR lung region label images used to train the segmentation model; based on the trained segmentation model, segment the left and right lungs of the two-dimensional DR lung images at multiple time points during the breathing process to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points; and / or, the method for determining the DR lung region label images used to train the segmentation model includes: performing costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edge detection on 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.

[0017] According to one aspect of this disclosure, a method for assessing lung development is provided, comprising: obtaining the left lung area and / or right lung area during the respiratory process using the determination method or the determination device described above;

[0018] Acquire the patient's age, height, and gender information corresponding to the left and / or right lung areas during the breathing process, and determine the set left and right lung field areas during the breathing process based on the age, height, and gender information.

[0019] Lung development was assessed in the patient based on the left lung field area, right lung field area, a predetermined left lung field area, and a predetermined right lung field area during the patient's breathing process; and / or,

[0020] The method for assessing lung development in the patient based on the left lung field area, right lung field area, and a predetermined left and right lung field area during the patient's breathing process includes:

[0021] If the area of ​​the left lung field during respiration is smaller than the corresponding set left lung field area during respiration, then the patient's left lung is abnormally developed; otherwise, the patient's left lung is normally developed; or, calculate the average left lung area corresponding to the area of ​​the left lung field during respiration and calculate the set average left lung area corresponding to the set left lung field area during respiration; if the average left lung area is smaller than the set average left lung area, then the patient's left lung is abnormally developed; otherwise, the patient's left lung is normally developed; and / or,

[0022] If the area of ​​the right lung field during respiration is smaller than the corresponding set right lung field area during respiration, then the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal; or, calculate the average right lung area corresponding to the area of ​​the right lung field during respiration and calculate the set average right lung area corresponding to the set right lung field area during respiration; if the average right lung area is smaller than the set average right lung area, then the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal; and / or,

[0023] This also includes: under the condition that, based on the lung field area of ​​the left lung, the lung field area of ​​the right lung, a predetermined lung field area, and a predetermined lung field area during the patient's breathing process, the result of the lung development assessment of the patient is that the right lung development is normal and / or the right lung development is normal.

[0024] The left lung area and / or right lung area corresponding to the patient's deep inhalation and deep exhalation during the breathing process are obtained, and the corresponding left lung area setting value and / or right lung area setting value are determined according to the age information, height information and gender information.

[0025] Lung development is assessed in the patient based on the left lung area and a set value for the left lung area during deep inspiration and deep expiration, and / or based on the right lung area and a set value for the right lung area; and / or,

[0026] The method based on the area of ​​the left lung and a set value for the area of ​​the left lung includes:

[0027] Calculate the first difference between the left lung area during deep inhalation and the left lung area during deep exhalation;

[0028] The first difference and the set area of ​​the left lung are used to assess lung development in the patient; and / or,

[0029] The method for assessing lung development in the patient, based on the first difference and the set value of the left lung area, includes:

[0030] If the first difference is greater than or equal to the set area of ​​the left lung, then the patient's left lung is abnormally developed; otherwise, the patient's left lung is normally developed; and / or,

[0031] The method based on the area of ​​the right lung and a set value for the area of ​​the right lung includes:

[0032] Calculate the second difference between the right lung area during deep inspiration and the right lung area during deep expiration;

[0033] The second difference and the set area of ​​the right lung are used to assess lung development in the patient; and / or,

[0034] The method for assessing lung development in the patient based on the second difference and the set area of ​​the right lung includes:

[0035] If the second difference is greater than or equal to the set value of the right lung area, then the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal.

[0036] According to one aspect of this disclosure, a lung field area determination device is provided, comprising:

[0037] The first acquisition unit is used to acquire two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple moments during the breathing process;

[0038] The determining unit is configured to determine the area of ​​the left lung and / or the area of ​​the right lung during the respiratory process based on two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple time points during the respiratory process; and / or,

[0039] The determining unit includes:

[0040] A set area acquisition unit is used to acquire the set area corresponding to a single pixel;

[0041] The statistical unit is used to count the number of pixels in the left lung in the two-dimensional DR left lung image and / or the number of pixels in the two-dimensional DR right lung image at multiple moments during the breathing process.

[0042] The area calculation unit is used to calculate, based on the set area, the left lung area and / or right lung area corresponding to the number of left lung pixels in the two-dimensional DR left lung image and / or the right lung pixel number in the two-dimensional DR right lung image at multiple time points during the breathing process; and / or,

[0043] The area calculation unit includes: a left lung area calculation unit and / or a right lung area calculation unit;

[0044] The left lung area calculation unit is used to multiply the set area by the number of left lung pixels in the two-dimensional DR left lung images at multiple moments during the breathing process to obtain the corresponding left lung area during the breathing process; and / or, the right lung area calculation unit is used to multiply the set area by the number of right lung pixels in the two-dimensional DR right lung images at multiple moments during the breathing process to obtain the corresponding right lung area during the breathing process; and / or,

[0045] It also includes: segmentation units;

[0046] The segmentation unit is configured to, before acquiring the two-dimensional DR lung images at multiple moments during the respiratory process and / or the two-dimensional DR lung images at multiple moments during the respiratory process, segment the two-dimensional DR lung images at multiple moments during the respiratory process into left and right lungs, thereby obtaining the two-dimensional DR left lung images and two-dimensional DR right lung images at multiple moments; and / or,

[0047] The segmentation unit includes: a detection unit;

[0048] The detection unit is used to detect the costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edges of the left and right chest images of the two-dimensional DR lung images at multiple time points during the breathing process, respectively, to obtain the two-dimensional DR left lung image and the two-dimensional DR right lung image at multiple time points; or,

[0049] The segmentation unit includes: a model and data acquisition unit, a training unit, and an output unit;

[0050] The model and data acquisition unit are used to acquire a segmentation model of a preset convolutional neural network and a DR lung region label image used to train the segmentation model.

[0051] The training unit is used to train the segmentation model using the DR lung region label image used to train the segmentation model.

[0052] The output unit is used to segment the left and right lungs of the two-dimensional DR lung images at multiple time points during the breathing process based on the trained segmentation model, thereby obtaining two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points; and / or,

[0053] The segmentation unit further includes: a label determination unit;

[0054] The label determination unit is used to detect the costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edges of the left and right chest images of multiple DR lung area images, respectively, to obtain DR lung area label images corresponding to the multiple DR lung area images.

[0055] According to one aspect of this disclosure, a lung development assessment device is provided, comprising: obtaining the left lung area and / or right lung area during the respiratory process by means of or application of the lung field area determination method as described above or the lung field area determination device as described above; obtaining the left lung area and / or right lung area during the respiratory process; and,

[0056] The second acquisition unit is used to acquire the patient's age, height and gender information corresponding to the left lung area and / or right lung area during the breathing process, and to determine the set left lung field area and set right lung field area during the breathing process based on the age, height and gender information.

[0057] The first assessment unit is used to assess the lung development of the patient based on the left lung field area, right lung field area, a predetermined left lung field area, and a predetermined right lung field area during the patient's breathing process; and / or,

[0058] The first assessment unit further includes: a left lung assessment unit and a right lung assessment unit;

[0059] The left lung assessment unit is configured to determine if the left lung field area during respiration is smaller than a corresponding set left lung field area during respiration, indicating abnormal left lung development in the patient; otherwise, normal left lung development in the patient; or, the left lung assessment unit is configured to calculate the average left lung area corresponding to the left lung field area during respiration and the set average left lung area corresponding to a set left lung field area during respiration; if the average left lung area is smaller than the set average left lung area, the patient's left lung development is abnormal; otherwise, normal left lung development in the patient; and / or,

[0060] The right lung assessment unit is configured to determine if the right lung field area during respiration is smaller than a corresponding set right lung field area during respiration, indicating that the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal. Alternatively, the right lung assessment unit is configured to calculate the average right lung area corresponding to the right lung field area during respiration and the set average right lung area corresponding to a set right lung field area during respiration; if the average right lung area is smaller than the set average right lung area, the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal. And / or,

[0061] It also includes: a condition discrimination unit;

[0062] The condition discrimination unit is used to assess the patient's lung development based on the left lung field area, right lung field area, a preset left lung field area, and a preset right lung field area during the patient's breathing process, and the result is that the right lung development is normal and / or the right lung development is normal.

[0063] The second acquisition unit is used to acquire the left lung area and / or right lung area corresponding to the patient's deep inhalation and deep exhalation during the breathing process, and to determine the corresponding left lung area setting value and / or right lung area setting value according to the age information, height information and gender information.

[0064] Lung development is assessed in the patient based on the left lung area and a set value for the left lung area during deep inspiration and deep expiration, and / or based on the right lung area and a set value for the right lung area; and / or,

[0065] The second assessment unit includes: a second left lung assessment unit;

[0066] The second left lung assessment unit, used to assess the patient's lung development based on the left lung area and a set value for the left lung area, includes: a first calculation unit and a first comparison unit; wherein, the first calculation unit is used to calculate a first difference between the left lung area during deep inspiration and the left lung area during deep expiration; wherein, the first comparison unit is used to assess the patient's lung development based on the first difference and a set value for the left lung area; and / or,

[0067] The first comparison unit, based on the first difference and a set value for the left lung area, performs a lung development assessment on the patient, including: if the first difference is greater than or equal to the set value for the left lung area, then the patient's left lung development is abnormal; otherwise, the patient's left lung development is normal; and / or,

[0068] The second assessment unit further includes: a second right lung assessment unit;

[0069] The second right lung assessment unit, used to assess the patient's lung development based on the right lung area and a set value for the right lung area, includes: a second calculation unit and a second comparison unit; wherein, the second calculation unit is used to calculate a second difference between the right lung area during deep inspiration and the right lung area during deep expiration; the second comparison unit is used to assess the patient's lung development based on the second difference and the set value for the right lung area; and / or,

[0070] The second comparison unit is used to assess the patient's lung development based on the second difference and the set value of the right lung area, including: if the second difference is greater than or equal to the set value of the right lung area, then the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal.

[0071] According to one aspect of this disclosure, an electronic device is provided, comprising:

[0072] processor;

[0073] Memory used to store processor-executable instructions;

[0074] The processor is configured to execute the above-described method for determining lung field area in DR images and / or the above-described method for assessing lung development.

[0075] 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 method for determining lung field area in DR images and / or the above-described method for assessing lung development.

[0076] In the embodiments of this disclosure, the methods and devices for determining lung field area and assessing lung development in DR images, as well as the electronic devices and storage media, are provided to improve the intelligent assessment level of DR lung images and solve the problem that DR lung images are not widely used in lung development assessment.

[0077] 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.

[0078] 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

[0079] 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.

[0080] Figure 1 A flowchart illustrating a method for determining lung field area according to an embodiment of the present disclosure is shown.

[0081] Figure 2 A flowchart illustrating a lung development assessment method according to an embodiment of the present disclosure is shown;

[0082] Figure 3 A block diagram of a lung field area determination device according to an embodiment of the present disclosure is shown;

[0083] Figure 4 A block diagram of a lung development assessment device according to an embodiment of the present disclosure is shown;

[0084] Figure 5 This is a block diagram illustrating an electronic device 800 according to an exemplary embodiment;

[0085] Figure 6 This is a block diagram illustrating an electronic device 1900 according to an exemplary embodiment. Detailed Implementation

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] In addition, this disclosure also provides a device for determining the lung field area in DR images, a lung development assessment device, an electronic device, a computer-readable storage medium, and a program. All of the above can be used to implement any of the methods for determining the lung field area in DR images or for assessing lung development provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding section of the method and will not be repeated here.

[0092] Figure 1 A flowchart illustrating a method for determining the lung field area in DR images according to an embodiment of the present disclosure is shown, as follows: Figure 1 As shown, the method for determining the lung field area in a DR image includes: Step S101: acquiring two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple moments during the respiratory process; Step S102: determining the left lung area and / or right lung area during the respiratory process based on the two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple moments during the respiratory process. This improves the intelligent assessment level of DR lung images, addressing the current problem of DR lung images not being widely used in lung development assessment.

[0093] Step S101: Acquire two-dimensional DR images of the left lung and / or two-dimensional DR images of the right lung at multiple moments during the breathing process.

[0094] In the embodiments of this disclosure and other possible embodiments, digital X-ray (DR) imaging equipment can provide high-resolution and real-time X-ray images and has been widely used in examinations of the skeletal system, chest, dentistry, etc., such as fracture diagnosis, lung disease screening, and dental X-rays. Therefore, DR imaging equipment can be used to image the skeletal system, chest, dentistry, etc.

[0095] In embodiments of this disclosure, before acquiring two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple moments during the breathing process, the two-dimensional DR lung images at multiple moments during the breathing process are acquired, and the two-dimensional DR lung images at multiple moments during the breathing process are segmented into left and right lungs to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple moments.

[0096] In embodiments of this disclosure, the method for segmenting the left and right lungs of multi-time 2D DR lung images during respiration to obtain multi-time 2D DR left lung images and 2D DR right lung images includes: performing costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edge detection on the left and right chest images of the multi-time 2D DR lung images during respiration, respectively, to obtain multi-time 2D DR left lung images and 2D DR right lung images; or, the method for segmenting the left and right lungs of the multi-time 2D DR lung images during respiration to obtain multi-time 2D DR left lung images and 2D DR right lung images includes: obtaining a segmentation model from a preset convolutional neural network. The model and DR lung region label images used to train the segmentation model; the segmentation model is trained using the DR lung region label images used to train the segmentation model; based on the trained segmentation model, the left and right lungs of the two-dimensional DR lung images at multiple time points during the breathing process are segmented to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points; and / or, the method for determining the DR lung region label images used to train the segmentation model includes: performing costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edge detection on 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.

[0097] In embodiments of this disclosure and other possible embodiments, a DR image to be processed (a two-dimensional DR image of the left lung at multiple moments during breathing or breath-holding) is acquired, and it is determined whether the DR image to be processed is a lung image; wherein the lung image is configured as a two-dimensional DR lung image at multiple moments during breathing; if it is the lung image, a thoracic cavity detection is performed on the DR image to be processed to remove information outside the thoracic cavity from the DR image to be processed.

[0098] In embodiments of this disclosure and other possible embodiments, the method for determining whether a DR image to be processed is a lung image includes: calculating the average grayscale value corresponding to the DR image to be processed, and determining whether it is a lung image based on the average grayscale value and a set grayscale value. Those skilled in the art can configure the set grayscale value according to actual needs. For example, the set grayscale value can be configured to any value or range between -1000HU and 0HU. Alternatively, in embodiments of this disclosure and other possible embodiments, the determination of whether a DR image to be processed is a lung image can also be made by manual judgment of the DR image to be processed.

[0099] In the embodiments of this disclosure and other possible embodiments, the method for determining whether an image is a lung image based on the average gray value and a set gray value includes: if the DR image to be processed has not undergone inversion processing, then if the average gray value is less than or equal to the set gray value, the DR image to be processed is determined to be a lung image; if the DR image to be processed has undergone inversion processing, then if the average gray value is greater than or equal to the set gray value, the DR image to be processed is determined to be a lung image. The principle is that if it is a lung image, the lungs in the lung image will generally be filled with or contain a certain amount of air, and air corresponds to a relatively small gray value (CT value), generally configured as -1000 HU; while the gray value (CT value) of water is generally configured as 0 HU, and the gray value (CT value) of bone is generally configured as 1000 HU or higher; therefore, the above technical solution is used to determine whether the DR image to be processed is a lung image.

[0100] In embodiments of this disclosure and other possible embodiments, if the image is a lung image, then a thoracic cavity detection is performed on the DR image to be processed to remove information outside the thoracic cavity from the DR image to be processed. In embodiments of this disclosure and other possible embodiments, the information outside the thoracic cavity includes: removing unnecessary information such as arms, heads, and blank backgrounds.

[0101] In the embodiments of this disclosure, before performing thoracic cavity detection on the DR image to be processed (the lung image to be segmented or the two-dimensional DR lung image at multiple moments during breathing or breath-holding), the DR image to be processed is filtered, and the filtered lung image to be segmented is downsampled to a set size.

[0102] In embodiments of this disclosure, image enhancement is performed on the logarithmic transformation of the DR image to be processed, which is of a set size, to obtain an enhanced DR image to be processed.

[0103] (1) Image preprocessing of DR images to be processed (lung images to be segmented or two-dimensional DR lung images at multiple moments during the breathing process).

[0104] In the embodiments of this disclosure and other possible embodiments, a. a DR image (lung image to be segmented) is processed, and a low-pass filter is applied to the DR image (lung image to be segmented). The filtered lung image to be segmented is then downsampled to a set size to speed up image processing. A logarithmic transformation is performed on the downsampled lung image to enhance the image, resulting in an enhanced lung image to be segmented. The low-pass template used is Gaussian filtering or mean filtering, and the set size value can be configured as 3×3 or 5×5. The downsampling range can be configured to 2-6 times. Those skilled in the art can configure the set size value and / or the downsampling range according to actual needs.

[0105] In the embodiments of this disclosure and other possible embodiments, b. the technical solution for detecting the thoracic cavity in the DR image to be processed adopts adaptive determination of the thoracic cavity contour based on the characteristics of the lung image to be segmented, and removes unnecessary information such as arms, head and blank background.

[0106] In the embodiments of this disclosure, step 1) of the method for performing DR image processing on the DR image to be processed includes: calculating a plurality of first gradient magnitudes corresponding to the horizontal direction of each pixel in the DR image to be processed and a plurality of second gradient magnitudes corresponding to the vertical direction of each pixel; determining a plurality of total gradient magnitudes based on the plurality of first gradient magnitudes and the plurality of second gradient magnitudes; integrating the plurality of first gradient magnitudes corresponding to the horizontal direction and the plurality of second gradient magnitudes corresponding to the vertical direction along a direction perpendicular to them to obtain a plurality of first integral values ​​and a plurality of second integral values; and integrating the plurality of total gradient magnitudes. The integral is divided into two directions corresponding to the longitudinal and transverse directions to obtain multiple third integral values ​​and multiple fourth integral values; multiple first local maxima corresponding to the multiple first ratios are calculated, and multiple first local minima and multiple second local minima corresponding to the multiple first ratios and multiple second ratios are calculated; the thoracic profile corresponding to the DR image to be processed is determined based on the multiple first local maxima, the multiple first local minima, the multiple second local minima, and the thoracic profile features; wherein, the thoracic profile features can be configured with a first segmentation position of the neck or shoulder corresponding to the thoracic profile and a second segmentation position on both sides of the thoracic profile.

[0107] In embodiments of this disclosure, the method for calculating multiple first gradient magnitudes in the horizontal direction and multiple second gradient magnitudes in the vertical direction of each pixel in the DR image to be processed includes: obtaining a gradient operator; and using the gradient operator to calculate multiple first gradient magnitudes in the horizontal direction and multiple second gradient magnitudes in the vertical direction of each pixel in the DR image to be processed.

[0108] In embodiments of this disclosure, the method for determining multiple total gradient magnitudes based on the plurality of first gradient magnitudes and the plurality of second gradient magnitudes includes: calculating a plurality of first sums of squares corresponding to the plurality of first gradient magnitudes and a plurality of second sums of squares corresponding to the plurality of second gradient magnitudes, and determining the plurality of total gradient magnitudes based on the plurality of first sums of squares and the plurality of second sums of squares; and / or, the method for determining the plurality of total gradient magnitudes based on the plurality of first sums of squares and the plurality of second sums of squares includes: summing the plurality of first sums of squares and the plurality of second sums of squares, and taking the square root of the sums to obtain the plurality of total gradient magnitudes.

[0109] In embodiments of this disclosure and other possible embodiments, a plurality of first gradient magnitudes corresponding to the horizontal direction of each pixel in the lung image to be segmented and a plurality of second gradient magnitudes corresponding to the vertical direction of each pixel are calculated respectively; and a plurality of total gradient magnitudes are determined based on the plurality of first gradient magnitudes and the plurality of second gradient magnitudes; wherein, the method of determining the plurality of total gradient magnitudes based on the plurality of first gradient magnitudes and the plurality of second gradient magnitudes includes: calculating a plurality of first sums of squares corresponding to the plurality of first gradient magnitudes and a plurality of second sums of squares corresponding to the plurality of second gradient magnitudes respectively, and determining the plurality of total gradient magnitudes based on the plurality of first sums of squares and the plurality of second sums of squares. The method of determining the plurality of total gradient magnitudes based on the plurality of first sums of squares and the plurality of second sums of squares includes: summing the plurality of first sums of squares and the plurality of second sums of squares respectively, and taking the square root of the sums to obtain the plurality of total gradient magnitudes.

[0110] For example, each pixel e in the lung image to be segmented and its eight-neighbor matrix are: Using the Sobel gradient operator Calculate the magnitudes of the first gradient (c + 2*f + ia - 2*dg) in the horizontal direction for each pixel e in the lung image to be segmented, and then use the transpose of the Sobel gradient operator. Calculate the magnitudes of the second gradient in the vertical direction (g + 2*h + ia - 2*bc) for each pixel e in the lung image to be segmented. Additionally, those skilled in the art may choose other gradient operators, such as the Roberts gradient operator or the Laplace gradient operator, depending on the specific needs.

[0111] For example, based on the plurality of first gradient magnitudes (h1, h2, ..., h... n and the plurality of second gradient magnitudes (k1,k2,...,k n Determine multiple total gradient magnitudes.

[0112] Step 2). Integrate the multiple first gradient magnitudes corresponding to the horizontal direction and the multiple second gradient magnitudes corresponding to the vertical direction along the direction perpendicular to them to obtain multiple first integral values ​​and multiple second integral values;

[0113] Specifically, multiple first gradient magnitudes corresponding to the horizontal direction are integrated in the vertical direction to obtain multiple first integral values; and multiple second integral values ​​are obtained by integrating multiple first gradient magnitudes corresponding to the vertical direction in the horizontal direction.

[0114] Step 3). Integrate the multiple total gradient magnitude integrals in the two corresponding directions, the vertical and horizontal directions, respectively, to obtain multiple third integral values ​​and multiple fourth integral values; wherein, integrating the multiple total gradient magnitude integrals in the vertical direction yields multiple third integral values; and integrating the multiple total gradient magnitude integrals in the horizontal direction yields multiple fourth integral values.

[0115] Step 4). (a) When determining multiple first local maxima, calculate the ratio based on the results of Step 2 and Step 3. When the ratio is less than a preset value, the image information at this location is considered noise and needs to be discarded. The noise can be configured to be image information corresponding to unnecessary information such as arms, heads, and blank backgrounds.

[0116] Specifically, based on the plurality of first integral values ​​and the plurality of third integral values, it is determined whether each of the plurality of first ratios corresponding to the plurality of first integral values ​​and the plurality of third integral values ​​in the horizontal direction should be retained.

[0117] The method for determining whether to retain each of the plurality of first ratios corresponding to the plurality of first integral values ​​and the plurality of third integral values ​​in the horizontal direction, based on the plurality of first integral values ​​and the plurality of third integral values, includes: obtaining a first preset value; calculating the plurality of first ratios between the plurality of first integral values ​​and the corresponding plurality of third integral values ​​in the horizontal direction; and discarding the first ratio if it is less than the first preset value. In embodiments of this disclosure and other possible embodiments, those skilled in the art may configure the first preset value as needed.

[0118] (b) When determining multiple first local minima, calculate the ratio based on the results of step 2 and step 3. When the ratio is greater than a preset value, the image information at this location is considered noise and needs to be discarded. The noise can be configured to include image information corresponding to unnecessary information such as arms, heads, and blank backgrounds.

[0119] Specifically, based on the plurality of first integral values ​​and the plurality of third integral values, it is determined whether each of the plurality of first ratios corresponding to the plurality of first integral values ​​and the plurality of third integral values ​​in the horizontal direction should be retained.

[0120] The method for determining whether to retain each of the plurality of first ratios corresponding to the plurality of first integral values ​​and the plurality of third integral values ​​in the horizontal direction, based on the plurality of first integral values ​​and the plurality of third integral values, includes: obtaining a first preset value; calculating the plurality of first ratios between the plurality of first integral values ​​and the corresponding plurality of third integral values ​​in the horizontal direction; and discarding the first ratio if one of the plurality of first ratios is greater than the first preset value.

[0121] Specifically, based on the plurality of second integral values ​​and the plurality of fourth integral values, it is determined whether each of the plurality of second ratios corresponding to the plurality of second integral values ​​and the plurality of fourth integral values ​​in the longitudinal direction should be retained.

[0122] The method for determining whether to retain each of the plurality of second ratios corresponding to the plurality of second integral values ​​and the plurality of fourth integral values ​​in the longitudinal direction, based on the plurality of second integral values ​​and the plurality of fourth integral values, includes: obtaining a second preset value; calculating the plurality of second ratios between the plurality of second integral values ​​and the corresponding plurality of fourth integral values ​​in the longitudinal direction; and discarding a certain second ratio if it is greater than the second preset value. In embodiments of this disclosure and other possible embodiments, those skilled in the art may configure the second preset value as needed.

[0123] In embodiments of this disclosure, before calculating the plurality of first local maxima corresponding to the plurality of first ratios, and calculating the plurality of first local minima and the plurality of second local minima corresponding to the plurality of first ratios and the plurality of second ratios, it is determined, based on the plurality of first integral values ​​and the plurality of third integral values, whether each of the plurality of first ratios corresponding to the plurality of first integral values ​​and the plurality of third integral values ​​in the horizontal direction should be retained; and, based on the plurality of second integral values ​​and the plurality of fourth integral values, it is determined whether each of the plurality of second ratios corresponding to the plurality of second integral values ​​and the plurality of fourth integral values ​​in the vertical direction should be retained; then, the plurality of first local maxima corresponding to the retained plurality of first ratios are calculated, and the plurality of first local minima and the plurality of second local minima corresponding to the retained plurality of first ratios and the retained plurality of second ratios are calculated.

[0124] In embodiments of this disclosure, the differential derivatives of the first curves corresponding to the plurality of first ratios are calculated to obtain the plurality of first local maxima and the plurality of first local minima; and the differential derivatives of the second curves corresponding to the plurality of second ratios are calculated to obtain the plurality of second local minima.

[0125] In embodiments of this disclosure, the method for determining the thoracic map corresponding to the lung image to be segmented based on a plurality of first local maxima, a plurality of first local minima, a plurality of second local minima, and thoracic features includes: determining second segmentation positions on both sides of the thoracic cavity based on the plurality of first local maxima and the thoracic features; determining first segmentation positions of the neck or shoulder based on the plurality of second local minima and the thoracic features; and determining the thoracic map corresponding to the lung image to be segmented based on the first segmentation positions and the first segmentation positions.

[0126] In the embodiments of this disclosure and other possible embodiments, 5) calculate the plurality of first local maxima corresponding to the plurality of first ratios discarded in step 4(a). Similarly, calculate the plurality of first local minima and the plurality of second local minima corresponding to the plurality of first ratios and the plurality of second ratios discarded in step 4(b). Wherein, the plurality of first ratios discarded are the plurality of first ratios retained after discarding; similarly, the plurality of second ratios discarded are the plurality of second ratios retained after discarding.

[0127] The thoracic profile corresponding to the lung image to be segmented is determined based on multiple first local maxima, multiple first local minima, multiple second local minima, and thoracic profile features, removing unnecessary information such as arms, head, and blank background. The thoracic profile features can be configured with a first segmentation position corresponding to the neck or shoulder and second segmentation positions on both sides of the thoracic profile.

[0128] The method for determining the thoracic map corresponding to the lung image to be segmented based on multiple first local maxima, multiple first local minima, multiple second local minima, and thoracic features includes: determining second segmentation positions on both sides of the thoracic cavity based on the multiple first local maxima and the thoracic features; determining first segmentation positions of the neck or shoulder based on the multiple second local minima and the thoracic features; and determining the thoracic map corresponding to the lung image to be segmented based on the first segmentation positions.

[0129] The plurality of first local maxima are obtained by calculating the differential derivatives of the first curves corresponding to the plurality of discarded first ratios; wherein the differential derivatives can be configured as first-order, second-order, or other multi-order differential derivatives. Similarly, the plurality of first local minima are obtained by calculating the differential derivatives of the first curves corresponding to the plurality of discarded first ratios; wherein the differential derivatives can be configured as first-order, second-order, or other multi-order differential derivatives. Similarly, the plurality of second local minima are obtained by calculating the differential derivatives of the second curves corresponding to the plurality of discarded second ratios; wherein the differential derivatives can be configured as first-order, second-order, or other multi-order differential derivatives.

[0130] In embodiments of this disclosure, the method for determining the second segmentation positions on both sides of the thorax based on the plurality of first local maxima and the thorax features includes: determining the maximum value among all the plurality of first local maxima on one side of the centerline of the DR image to be processed, and configuring the position information corresponding to the maximum value as the segmentation position point of the side to be determined corresponding to the side of the thorax; determining the minimum value among all the plurality of first local minima on the other side of the centerline of the DR image to be processed, and configuring the position information corresponding to the minimum value as the segmentation position point of the other side to be determined corresponding to the other side of the thorax.

[0131] In embodiments of this disclosure, a method for determining the first segmentation position of the neck or shoulder based on the plurality of second local minima and the thoracic features includes: configuring the position information corresponding to the minimum value of the plurality of second local minima as the first segmentation position of the neck or shoulder.

[0132] In embodiments of this disclosure and other possible embodiments, the method for determining the second segmentation positions on both sides of the thorax based on the plurality of first local maxima and the thorax features includes: determining the maximum value among all the plurality of first local maxima on one side (right side) of the center line of the image to be segmented, and configuring the position information corresponding to the maximum value as the segmentation position point of the side to be determined corresponding to the side of the thorax; determining the minimum value among all the plurality of first local minima on the other side (left side) of the center line of the image to be segmented, and configuring the position information corresponding to the minimum value as the segmentation position point of the other side to be determined corresponding to the other side of the thorax.

[0133] For example, determine all of the plurality of first local maxima (a1, a2, ..., a) on one side (right side) of the centerline. n The maximum value a in ) m Where m is less than or equal to n; m is the location information corresponding to the maximum value (e.g., a certain horizontal coordinate), that is, the segmentation point of the side to be determined corresponding to one side of the thorax. Similarly, determine all the plurality of first local minima (b1, b2, ..., b) on the other side (left side) of the centerline. n The minimum value b in ) r , where r is less than or equal to n; r is the location information (e.g., a certain horizontal coordinate) corresponding to the minimum value.

[0134] In embodiments of this disclosure and other possible embodiments, a method for determining a first segmentation position of the neck or shoulder based on the plurality of second local minima and the thoracic features includes: configuring the position information (e.g., a certain ordinate) corresponding to the minimum value of the plurality of second local minima as the first segmentation position of the neck or shoulder.

[0135] (2) Lung field segmentation.

[0136] In the embodiments of this disclosure, the thoracic image obtained by thoracic cavity detection of the DR image to be processed is configured as the lung image to be segmented; and the left and right lungs are segmented based on the lung image to be segmented.

[0137] In embodiments of this disclosure, the lung image to be segmented 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.

[0138] In embodiments of this disclosure, the lung image to be segmented 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 segmentation model of a preset convolutional neural network, DR lung region label images used to train the segmentation model, and multiple DR lung images (lung images to be segmented) at multiple times during breathing or breath-holding; wherein, the method for determining the DR lung region label images used to train the segmentation model includes: performing costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edge detection on the left chest image and right chest image of the 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 completing the left lung and / or right lung segmentation of the multiple DR lung images to be segmented based on the trained segmentation model.

[0139] In embodiments of this disclosure and other possible embodiments, the segmentation model of the preset convolutional neural network is 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.

[0140] 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.

[0141] In embodiments of this disclosure and other possible embodiments, the multiple DR lung area images are configured to be acquired during deep inspiration or breath-holding.

[0142] 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.

[0143] 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.

[0144] 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 configuration operation on the arbitrary two DR lung region label images to obtain a corresponding DR lung region label registration image; and performing a fusion operation on the DR lung region label registration image to obtain the enhanced DR lung region label image.

[0145] 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.

[0146] In the embodiments and other possible embodiments of this disclosure, the registration method used in this disclosure can employ existing registration algorithms or models, such as one or more of SIFT (Scale-invariant feature transform), SURF (Speeded Up Robust Features), and 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 convolutional neural networks can be configured as a registration algorithm or model based on VGG networks.

[0147] c. Based on the chest image corresponding to the lung image to be segmented, the chest image is divided into two parts: a left chest image and a right chest image. Based on this, the left lung field of the left chest image is segmented and the right lung field of the right chest image is segmented.

[0148] In embodiments of this disclosure, a method for segmenting the left and right lungs based on the left chest image and the right chest image respectively includes: detecting the costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edges of the left chest image and the right chest image respectively; obtaining a segmented image of the left lung based on the costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edges corresponding to the left chest image; and obtaining a segmented image of the right lung based on the costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edges corresponding to the right chest image.

[0149] In an embodiment of this disclosure, the method for determining the rib edge boundary of the left chest image includes: constructing a directional derivative template for the left chest image using directional derivatives, and setting a predetermined weighting depth for the directional derivative template; performing template traversal of the directional derivatives on the left chest image using the directional derivative template corresponding to the predetermined weighting depth, and superimposing the result of the template traversal onto the left chest image to obtain a superimposed left chest image; performing binarization processing on the superimposed left chest image to obtain a binary map of the left rib edge; obtaining a rib edge angle map of the left side to be filtered based on the binary map of the left rib edge and the superimposed left chest image; obtaining a filtered left rib edge angle map based on the rib edge angle map of the left side to be filtered and a first predetermined rib edge angle; and performing connected component selection on the left rib edge angle map to obtain the rib edge boundary corresponding to the largest connected component.

[0150] In an embodiment of this disclosure, the method for obtaining the left rib angle map to be screened based on the left rib edge binary image and the overlay image of the left chest includes: performing morphological opening and closing operations and thinning processing on the left rib edge binary image to obtain a morphologically processed left rib edge binary image; performing a bitwise AND operation on the gradient direction angle of each pixel in the morphologically processed left rib edge binary image and the overlay image of the left chest to obtain the left rib angle map to be screened.

[0151] In the embodiments of this disclosure, before constructing the directional derivative template of the left chest image using the directional derivative, the left chest image is subjected to Gaussian blurring at a set scale to obtain a corresponding Gaussian blurred image of the left chest; then, the directional derivative template of the Gaussian blurred image of the left chest is constructed using the directional derivative; during the process of defining the rib edge boundary of the left chest image, the template of the directional derivative corresponding to the set weighted depth is used to perform template traversal of the directional derivative of the Gaussian blurred image of the left chest, and the result of the template traversal is superimposed on the left chest image to obtain a Gaussian blurred superimposed image of the left chest; the Gaussian blurred superimposed image of the left chest is binarized to obtain a binary map of the left rib edge; based on the binary map of the left rib edge and the Gaussian blurred superimposed image of the left chest, the rib edge angle map of the left side to be screened is obtained.

[0152] a. In embodiments of this disclosure and other possible embodiments, the lung field segmentation steps are as follows, taking a left chest image as an example. For example, rib margin boundary detection in a left chest image.

[0153] 1) Apply a Gaussian blur of a predetermined scale to the left chest region (image) to reduce the detail information of this left chest region (image), resulting in a processed Gaussian blurred image of the left chest. The predetermined scale can be configured as 7×7 or 9×9, and those skilled in the art can configure the predetermined scale according to actual needs. The root mean square error of the Gaussian blur algorithm can be configured as a value such as 2, 2.5, or 3, and those skilled in the art can configure the root mean square error σ of the Gaussian blur algorithm according to actual needs.

[0154] 2) Construct a directional derivative template for the processed Gaussian blurred image f(x0,y0) of the left chest using the directional derivative, and set a weighted depth for the directional derivative template. Here, (x0,y0) represent the x-coordinate x0 and y-coordinate y0 of the coordinate point corresponding to the left chest image f(x0,y0), respectively.

[0155] The formula for calculating the directional derivative is as follows:

[0156]

[0157] Where l is a unit vector in the direction, and cosα and cosβ are the cosines of the l direction. The direction can be configured as a horizontal direction, α is the angle formed by the l direction and the horizontal direction, and β is the angle formed by the l direction and the vertical direction.

[0158] The method for constructing a directional derivative template of the processed Gaussian blurred image f(x0,y0) of the left chest using the directional derivative includes: obtaining the first radius in the x0 direction corresponding to the set template radius r. and the second radius in the y0 direction Based on the first radius and the second radius The directional derivative template of the processed Gaussian blurred image f(x0,y0) of the left chest is constructed using the directional derivative.

[0159]

[0160] in,

[0161]

[0162] For example, the set template radius can be configured to 1, 2, 3, 4, 5, etc. Those skilled in the art can configure the set template radius according to actual needs.

[0163] For example, the first radius r in the x0 direction x0 When configured as -1, 0, or 1, the second radius r in the y0 direction y0 The value range is -1, 0, and 1.

[0164] Those skilled in the art can configure the set weighted depth according to actual needs; for example, the set weighted depth can be configured to 6. Furthermore, the method for setting the set weighted depth of the directional derivative template includes: obtaining the set weighted depth; multiplying the set weighted depth by the directional derivative template to obtain the directional derivative template corresponding to the set weighted depth; and, based on the structural characteristics of the rib region, reasonably setting a range of directional angles and substituting it into the directional derivative calculation formula to obtain a template array.

[0165] 3) Using the directional derivative template corresponding to the weighted depth, perform directional derivative template traversal on the Gaussian blurred image of the left chest after step a.1, and superimpose the result of the template traversal (add the corresponding pixels) onto the Gaussian blurred image of the left chest in step 1 to obtain the Gaussian blurred superimposed image of the left chest.

[0166] For example, in the Gaussian blurred image of the left chest, each pixel e and its eight-neighbor matrix are: Using the directional derivative template corresponding to the weighted depth Calculate the superposition value of e for each pixel (e+w*(c+2*f+ia-2*dg)).

[0167] 4) Perform maximum inter-class variance binarization on the result of step a.3 (the Gaussian blurred image of the left chest) to obtain the rib edge binary image.

[0168] 5) Perform morphological opening and closing operations and thinning on the result of step a.4 (rib edge binary image) to obtain the morphologically processed rib edge binary image.

[0169] 6) Calculate the horizontal and vertical gradients of the results from step a.3 (the Gaussian blurred overlay image of the left chest) to obtain the horizontal gradient map and the vertical gradient map; based on the horizontal gradient map and the vertical gradient map, calculate the gradient direction angle of each pixel in the Gaussian blurred overlay image of the left chest.

[0170] 7) Perform a bitwise AND operation between the result of step a.5 (the morphologically processed binary image of the rib margin) and the result of step a.6 (the gradient direction angle of each pixel in the Gaussian blurred superimposed image of the left chest) to obtain the rib margin angle image to be screened.

[0171] 8) Based on the characteristics of the rib edge tissue, set a reasonable angle range (first set rib edge angle range), and perform angle filtering on the results obtained in step a.7 (rib edge angle diagram to be filtered) to obtain the filtered rib edge angle diagram.

[0172] 9) Based on the results in step a.8 (the filtered rib angle diagram), select the connected components to obtain the largest connected component, which is the rib portion (rib boundary).

[0173] Similarly, in the embodiments of this disclosure, the method for determining the rib edge boundary of the right chest image includes: constructing a directional derivative template of the right chest image using directional derivatives, and setting a predetermined weighting depth for the directional derivative template; performing template traversal of the directional derivatives of the right chest image using the directional derivative template corresponding to the predetermined weighting depth, and superimposing the result of the template traversal onto the right chest image to obtain a right chest overlay image; performing binarization processing on the right chest overlay image to obtain a right rib edge binary image; obtaining a rib edge angle image of the right side to be filtered based on the right rib edge binary image and the right chest overlay image; obtaining a filtered right rib edge angle image based on the rib edge angle image of the right side to be filtered and a second predetermined rib edge angle; and performing connected component selection on the right rib edge angle image to obtain the rib edge boundary corresponding to the largest connected component.

[0174] Similarly, in the embodiments of this disclosure, the method for obtaining the right rib angle map to be screened based on the right rib edge binary image and the right chest superimposed image includes: performing morphological opening and closing operations and thinning processing on the right rib edge binary image to obtain a morphologically processed right rib edge binary image; performing a bitwise AND operation on the gradient direction angle of each pixel in the morphologically processed right rib edge binary image and the right chest superimposed image to obtain the right rib angle map to be screened.

[0175] Similarly, in the embodiments of this disclosure, before constructing the directional derivative template of the right chest image using the directional derivative, the right chest image is subjected to Gaussian blurring at a set scale to obtain a corresponding right chest Gaussian blurred image; then, the directional derivative template of the right chest Gaussian blurred image is constructed using the directional derivative; during the process of defining the rib edge boundary of the right chest image, the directional derivative template corresponding to the set weighted depth is used to perform template traversal of the directional derivative of the right chest Gaussian blurred image, and the result of the template traversal is superimposed on the right chest image to obtain a right chest Gaussian blurred superimposed image; the right chest Gaussian blurred superimposed image is binarized to obtain a right rib edge binary image; based on the right rib edge binary image and the right chest Gaussian blurred superimposed image, the rib edge angle map of the right side to be screened is obtained.

[0176] b. Lung apex boundary detection. In an embodiment of this disclosure, the method for detecting the left lung apex boundary of the left chest image includes: determining a left lung apex detection region based on the left chest image; determining a left lung apex edge binary map based on the left lung apex detection region; and obtaining the fitted left lung apex boundary by fitting a quadratic function based on the left lung apex edge binary map.

[0177] In an embodiment of this disclosure, the method for determining the detection region of the left lung apex based on the left chest image includes: detecting a first coordinate corresponding to the uppermost coordinate point of the costal margin in the left chest image; the region formed by the first coordinate and the coordinate point of the uppermost right corner of the left chest image as the left lung apex detection region.

[0178] In the embodiments of this disclosure and other possible embodiments, 1) the coordinates of the uppermost coordinate point of the costal margin portion of the detected left chest image are used. 2) The rectangular area formed by the hypotenuse of this coordinate point and the coordinate point of the upper right corner of the left chest image is the lung apex detection area. For the lung apex detection area corresponding to the right lung, the rectangular area formed by the hypotenuse of the coordinate point of the uppermost coordinate point of the costal margin portion and the coordinate point of the upper left corner of the right chest image is the lung apex detection area corresponding to the right lung. 3) The lung apex area obtained in step b.2 is subjected to a set-scale Gaussian filter and contrast enhancement to obtain a filtered and enhanced lung apex area image. 4) Based on the angular characteristics of the lung apex edge, the binary image of the left lung apex edge is determined using the same method as steps a.2 to a.5 based on the filtered and enhanced lung apex area image. The angular characteristics of the lung apex edge are configured as a set angle range. 5) Based on the features of the lung apex edge and the binary image of the left lung apex edge, the Hough space parameters of a quadratic function are used for fitting to obtain the fitted lung apex edge (line). The edge of the lung apex is characterized as a quadratic function that opens downwards.

[0179] Similarly, in the embodiments of this disclosure, the method for detecting the right lung apex boundary of the right chest image includes: determining the right lung apex detection region based on the right chest image; determining the right lung apex edge binary map based on the right lung apex detection region; and obtaining the fitted right lung apex boundary by fitting a quadratic function based on the right lung apex edge binary map.

[0180] Similarly, in the embodiments of this disclosure, the method for determining the right lung apex detection region based on the right chest image includes: detecting the second coordinate corresponding to the uppermost coordinate point of the costal margin in the right chest image; the region formed by the second coordinate and the coordinate point of the upper left corner of the left chest image as the right lung apex detection region.

[0181] c. Mediastinal and diaphragmatic edge detection. In embodiments of this disclosure, a method for detecting the left lung mediastinal and diaphragmatic edges on the left chest image includes: binarizing the left chest image to obtain a left chest binary image; performing edge detection on the left chest binary image to obtain a left chest edge binary map; obtaining a left chest edge angle map based on the gradient direction angle of each pixel in the left chest binary image and the left chest edge binary map; obtaining selected left diaphragmatic and mediastinal edge angle maps based on the obtained left chest edge angle maps and setting the edge angle ranges of the diaphragm and mediastinum; and performing connected component selection processing based on the selected left diaphragmatic and mediastinal edge angle maps to obtain the left lung mediastinal and diaphragmatic edges corresponding to the largest connected component.

[0182] In an embodiment of this disclosure, the method for binarizing the left chest image to obtain a left chest binary image includes: performing contrast enhancement processing and maximum inter-class variance processing on the left chest Gaussian blurred image corresponding to the left chest image to obtain a left chest binary image.

[0183] In an embodiment of this disclosure, the method for determining the gradient direction angle of each pixel in the binary image of the left chest edge includes: calculating the horizontal and vertical gradients of the Gaussian blurred image of the left chest corresponding to the left chest image to obtain the horizontal gradient map and the vertical gradient map of the left chest; and obtaining the gradient direction angle of each pixel in the Gaussian blurred image of the left chest based on the horizontal gradient map and the vertical gradient map of the left chest.

[0184] In embodiments of this disclosure and other possible embodiments, a method for detecting the left lung mediastinum and diaphragm edges in the left chest image includes: 1) performing contrast enhancement processing and maximum inter-class variance processing on the processed left chest Gaussian blurred image obtained in step a.1 to obtain a left chest binary image; 2) performing morphological opening and closing operations and Canny edge detection sequentially on the result of step c.1 (left chest binary image) to obtain a left chest edge binary map; 3) calculating the horizontal and vertical gradients of the processed left chest Gaussian blurred image obtained in step a.1 to obtain a horizontal gradient map and a vertical gradient map; and calculating based on the horizontal and vertical gradient maps... 4) Perform an AND operation on the left chest binary image obtained in step c.1 and the left chest edge binary image obtained in step c.2, retaining the angles at the edge pixels to obtain the left chest edge angle map; 5) Select an appropriate angle range based on the edge characteristics of the diaphragm and diaphragm (set the edge angle range of the diaphragm and diaphragm), remove stray tissue edge information in the left chest edge angle map, and obtain the selected left diaphragm and diaphragm edge angle map; 6) Perform connected component selection processing based on the result of c.5 (the selected left diaphragm and diaphragm edge angle map) to obtain the largest connected component, which is the edge region of the diaphragm and diaphragm.

[0185] Similarly, in the embodiments of this disclosure, the method for detecting the right lung mediastinal and diaphragmatic edges of the right chest image includes: binarizing the right chest image to obtain a right chest binary image; performing edge detection on the right chest binary image to obtain a right chest edge binary map; obtaining a right chest edge angle map based on the gradient direction angle of each pixel in the right chest binary image and the right chest edge binary map; obtaining selected right diaphragmatic and mediastinal edge angle maps based on the obtained right chest edge angle maps and setting the edge angle range of the diaphragm and mediastinum; and performing connected component selection processing based on the selected right diaphragmatic and mediastinal edge angle maps to obtain the right lung mediastinal and diaphragmatic edges corresponding to the largest connected component.

[0186] Similarly, in the embodiments of this disclosure, the method of binarizing the right chest image to obtain a right chest binary image includes: performing contrast enhancement processing and maximum inter-class variance processing on the right chest Gaussian blurred image corresponding to the right chest image to obtain a right chest binary image.

[0187] Similarly, in the embodiments of this disclosure, the method for determining the gradient direction angle of each pixel in the binary image of the right chest edge includes: calculating the horizontal and vertical gradients of the right chest Gaussian blurred image corresponding to the right chest image to obtain the horizontal gradient map and the vertical gradient map of the right chest; and obtaining the gradient direction angle of each pixel in the right chest Gaussian blurred image based on the horizontal gradient map and the vertical gradient map of the right chest.

[0188] (3) Connection of the transverse and mediastinal edges, the lung apex and the costal margin. In the embodiments of this disclosure, the method for obtaining a segmented image of the left lung based on the costal margin boundary, lung apex boundary and mediastinal and transverse edges corresponding to the left chest image includes: calculating the first left lung apex edge point and the left lung mediastinal edge point corresponding to the shortest distance between the lung apex boundary and the mediastinal edge in the left chest image; calculating the second left lung apex edge point and the first left lung costal edge point corresponding to the shortest distance between the lung apex boundary and the costal margin edge in the left chest image; calculating the second costal edge point and the diaphragmatic edge point corresponding to the shortest distance between the costal margin boundary and the transverse edge in the left chest image; and obtaining a segmented image of the left lung based on the first left lung apex edge point, the left lung mediastinal edge point, the second left lung apex edge point, the first left lung costal edge point, the second costal edge point and the diaphragmatic edge point.

[0189] Similarly, in the embodiments of this disclosure, the method for obtaining a segmented image of the right lung based on the costal margin boundary, lung apex boundary, and mediastinal and transverse margins corresponding to the right chest image includes: calculating the first right lung apex edge point and the right lung mediastinal edge point corresponding to the shortest distance between the lung apex boundary and the mediastinal margin in the right chest image; calculating the second right lung apex edge point and the first right lung costal margin edge point corresponding to the shortest distance between the lung apex boundary and the costal margin boundary in the right chest image; calculating the second costal margin edge point and the diaphragmatic edge point corresponding to the shortest distance between the costal margin boundary and the transverse margin in the right chest image; and obtaining a segmented image of the right lung based on the first right lung apex edge point, the right lung mediastinal edge point, the second right lung apex edge point, the first right lung costal margin edge point, the second costal margin edge point, and the diaphragmatic edge point.

[0190] In embodiments of this disclosure and other possible embodiments, the method for connecting the transverse and mediastinal edges, the lung apex, and the costal margin includes: a. calculating the two points (the first lung apex edge point and the mediastinal edge point) with the shortest Euclidean distance between the lung apex edge (line) and the mediastinal edge (line), these two points being one of the endpoints of the lung apex and the transverse and mediastinal boundaries, respectively; b. calculating and obtaining the two points (the second lung apex edge point and the first costal edge edge point) with the shortest Euclidean distance between the lung apex edge (line) and the costal margin edge (line), these two points being one of the endpoints of the lung apex and the costal margin, respectively, and the lung apex region boundary can be obtained based on the other lung apex point obtained in step a; c. calculating and obtaining the costal margin (line) and the transverse and mediastinal edges... The two points with the shortest Euclidean distance between the edges (lines) of the diaphragm (the edge of the second costal margin and the edge of the transverse diaphragm) are respectively one of the endpoints of the costal margin and the transverse and mediastinal margins. The transverse and mediastinal margin boundary region can be obtained based on the other endpoint of the transverse and mediastinal margin boundary obtained in step a. The costal margin boundary region can be obtained based on the other endpoint of the costal margin obtained in step b. d. Connect the edge regions of the three parts obtained in steps a, b, and c above to obtain the closed lung field contour. The lung field region can be segmented based on the difference between the tissues inside and outside the contour boundary. e. The right lung field region is processed in the same way as above. Finally, the lung field region is mapped onto the original image to obtain the lung field segmentation of the original image.

[0191] Step S102: Determine the area of ​​the left lung and / or the area of ​​the right lung during the breathing process based on the two-dimensional DR left lung image and / or two-dimensional DR right lung image at multiple times during the breathing process.

[0192] In embodiments of this disclosure, the method for determining the area of ​​the left lung and / or the area of ​​the right lung during the breathing process based on two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple moments during the breathing process includes: obtaining a set area corresponding to a single pixel; counting the number of left lung pixels in the two-dimensional DR left lung images and / or the number of right lung pixels in the two-dimensional DR right lung images at multiple moments during the breathing process; and calculating the area of ​​the left lung and / or the area of ​​the right lung corresponding to the number of left lung pixels in the two-dimensional DR left lung images and / or the number of right lung pixels in the two-dimensional DR right lung images at multiple moments during the breathing process based on the set area.

[0193] In the embodiments of this disclosure and other possible embodiments, those skilled in the art can configure the set area corresponding to the individual pixel according to actual needs.

[0194] In embodiments of this disclosure, the method for calculating the left lung area and / or right lung area corresponding to the number of left lung pixels in a two-dimensional DR left lung image and / or the number of right lung pixels in a two-dimensional DR right lung image at multiple moments during the breathing process, based on the set area, includes: multiplying the set area by the number of left lung pixels in a two-dimensional DR left lung image at multiple moments during the breathing process to obtain the left lung area corresponding to the breathing process; and / or multiplying the set area by the number of right lung pixels in a two-dimensional DR right lung image at multiple moments during the breathing process to obtain the right lung area corresponding to the breathing process.

[0195] In addition, this disclosure also proposes a method for assessing lung development. Figure 2 A flowchart illustrating a lung development assessment method according to an embodiment of this disclosure is shown. Figure 2 As shown, the lung development assessment method includes: step S201: obtaining the left lung area and / or right lung area during the breathing process using the determination method described above; and step S202: obtaining the patient's age, height, and gender information corresponding to the left lung area and / or right lung area during the breathing process, and determining the set left lung field area and set right lung field area during the breathing process based on the age, height, and gender information; and step S203: assessing the patient's lung development based on the left lung field area, right lung field area, set left lung field area, and set right lung field area during the patient's breathing process, respectively.

[0196] In the embodiments of this disclosure and other possible embodiments, those skilled in the art can configure the set left lung field area and set right lung field area according to actual needs. For example, the age, height, and gender information corresponding to the patient for the lung development assessment should be consistent with or substantially consistent with the age, height, and gender information corresponding to the set left lung field area and set right lung field area.

[0197] For example, in the embodiments of this disclosure and other possible embodiments, the method for determining the set left lung field area and / or set right lung field area includes: acquiring the left lung field area and right lung field area corresponding to multiple age information, height information and gender information of normal lung development, respectively; calculating the average left lung field area and / or average right lung field area corresponding to height within the same gender and within a first set deviation range and age within a second set deviation range, respectively; and configuring the average left lung field area and / or the average right lung field area as the set left lung field area and / or set right lung field area, respectively.

[0198] In the embodiments of this disclosure and other possible embodiments, those skilled in the art can configure the first and second set deviation ranges according to actual needs. For example, the first set deviation range can be configured as 1-2 cm, and the second set deviation range as 1-3 years. That is, when separately calculating the average left lung field area and / or average right lung field area corresponding to height within the first set deviation range and age within the second set deviation range for the same sex, the age difference of the samples with normal lung development corresponding to the average left lung field area and / or average right lung field area is within 1-3 years, and the height difference is within 1-2 cm.

[0199] In embodiments of this disclosure, the method for assessing lung development in a patient based on the left lung field area, right lung field area, a predetermined left lung field area, and a predetermined right lung field area during the patient's breathing process includes: if the left lung field area during the breathing process is smaller than the corresponding predetermined left lung field area during the breathing process, then the patient's left lung development is abnormal; otherwise, the patient's left lung development is normal; or, calculating the average left lung area corresponding to the left lung field area during the breathing process and calculating the predetermined average left lung area corresponding to the predetermined left lung field area during the breathing process; if the average left lung area is smaller than the predetermined average left lung area, then the patient's left lung development is abnormal; otherwise, the patient's left lung development is normal.

[0200] In embodiments of this disclosure, if the area of ​​the right lung field during respiration is smaller than the corresponding set area of ​​the right lung field during respiration, then the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal; or, the average area of ​​the right lung corresponding to the area of ​​the right lung field during respiration and the set average area of ​​the right lung corresponding to the set area of ​​the right lung field during respiration are calculated respectively; if the average area of ​​the right lung is smaller than the set average area of ​​the right lung, then the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal.

[0201] In embodiments of this disclosure, the method further includes: under the condition that the lung development assessment of the patient is based on the left lung field area, right lung field area, and a set left lung field area and a set right lung field area during the patient's breathing process, and the result is that the right lung development is normal and / or the right lung development is normal, obtaining the left lung area and / or right lung area corresponding to the patient's deep inspiration and deep expiration during the breathing process, and determining the corresponding set value of the left lung area and / or the set value of the right lung area based on the age information, height information, and gender information; and performing a lung development assessment of the patient based on the left lung area and the set value of the left lung area during deep inspiration and deep expiration and / or based on the right lung area and the set value of the right lung area.

[0202] In the embodiments of this disclosure and other possible embodiments, the method for determining the set left lung field area and / or set right lung field area corresponding to deep inspiration and deep exhalation includes: acquiring the left lung field area and / or right lung field area corresponding to multiple age information, height information and gender information of normal lung development under deep inspiration and deep exhalation; calculating the average left lung field area and / or average right lung field area corresponding to height within a first set deviation range and age within a second set deviation range for the same gender; and configuring the average left lung field area and / or the average right lung field area as the set left lung field area and / or set right lung field area corresponding to deep inspiration and deep exhalation.

[0203] Similarly, in the embodiments of this disclosure and other possible embodiments, those skilled in the art can configure the first set deviation range and the second set deviation range according to actual needs. For example, the first set deviation range can be configured as 1-2 cm, and the second set deviation range can be configured as 1-3 years. That is, when separately calculating the average left lung field area and / or average right lung field area corresponding to height within the first set deviation range and age within the second set deviation range for the same sex under deep inspiration and deep expiration, the age difference of the samples with normal lung development corresponding to the average left lung field area and / or average right lung field area under deep inspiration and deep expiration is within 1-3 years, and the height difference is within 1-2 cm.

[0204] In embodiments of this disclosure, the method based on the left lung area and a set value for the left lung area includes: calculating a first difference between the left lung area during deep inspiration and the left lung area during deep expiration; and using the first difference and the set value for the left lung area to assess the patient's lung development. The method for assessing the patient's lung development using the first difference and the set value for the left lung area includes: if the first difference is greater than or equal to the set value for the left lung area, then the patient's left lung development is abnormal; otherwise, the patient's left lung development is normal.

[0205] In embodiments of this disclosure, the method based on the right lung area and a set value for the right lung area includes: calculating a second difference between the right lung area during deep inspiration and the right lung area during deep expiration; and using the second difference and the set value for the right lung area to assess the patient's lung development. The method of assessing the patient's lung development using the second difference and the set value for the right lung area includes: if the second difference is greater than or equal to the set value for the right lung area, then the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal.

[0206] In embodiments of this disclosure and other possible embodiments, the method for determining the set value of the left lung area includes: calculating the difference between the average left lung field area (set left lung field area) during deep inspiration and deep expiration to obtain the set value of the left lung area. Similarly, the method for determining the set value of the right lung area includes: calculating the difference between the average right lung field area (set right lung field area) during deep inspiration and deep expiration to obtain the set value of the right lung area.

[0207] In embodiments of this disclosure and other possible embodiments, the lung development assessment method includes: acquiring two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points during respiration; determining multiple left lung motion vertices and multiple right lung motion vertices based on the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points; determining multiple left lung diaphragm motion point sequences and multiple right lung diaphragm motion point sequences based on the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points; determining the motion of the left lung diaphragm based on the multiple left lung motion vertices and the multiple left lung diaphragm motion point sequences, and determining the motion of the right lung diaphragm based on the multiple right lung motion vertices and the multiple right lung diaphragm motion point sequences.

[0208] In embodiments of this disclosure, before determining the corresponding multiple left lung motion vertices and multiple right lung motion vertices based on the two-dimensional DR left lung image and the two-dimensional DR right lung image at multiple time points, and before determining the multiple left lung diaphragm motion point sequences and multiple right lung diaphragm motion point sequences based on the two-dimensional DR left lung image and the two-dimensional DR right lung image at multiple time points, the method further includes: extracting multiple left lung contour lines and multiple right lung contour lines corresponding to the two-dimensional DR left lung image and the two-dimensional DR right lung image at multiple time points; and determining the corresponding multiple left lung motion vertices, multiple right lung motion vertices, multiple left lung diaphragm motion point sequences and multiple right lung diaphragm motion point sequences based on the multiple left lung contour lines and the multiple right lung contour lines.

[0209] In embodiments of this disclosure and other possible embodiments, the method for extracting multiple left lung contour lines and multiple right lung contour lines corresponding to the two-dimensional DR left lung image and the two-dimensional DR right lung image at multiple time points includes: obtaining an edge detection algorithm or an edge detection model; and using the edge detection algorithm or the edge detection model to extract multiple left lung contour lines and multiple right lung contour lines corresponding to the two-dimensional DR left lung image and the two-dimensional DR right lung image at multiple time points.

[0210] In embodiments of this disclosure and other possible embodiments, the edge detection algorithm or edge detection model may be configured as one or more edge detection algorithms or edge detection models based on the Sobel operator, Prewitt operator, Roberts operator, Canny operator, Marr-Hildreth operator.

[0211] In an embodiment of this disclosure, the method for determining a plurality of left lung motion vertices and a plurality of right lung motion vertices based on the plurality of left lung contour lines and the plurality of right lung contour lines includes: detecting a plurality of left lung vertices and a plurality of right lung vertices corresponding to the plurality of left lung contour lines and the plurality of right lung contour lines respectively, and configuring the coordinates corresponding to the plurality of left lung vertices and the plurality of right lung vertices as the plurality of left lung motion vertices and the plurality of right lung motion vertices respectively.

[0212] In embodiments of this disclosure, the method for detecting multiple left lung vertices and multiple right lung vertices corresponding to the multiple left lung contour lines and the multiple right lung contour lines includes: determining multiple first maximum ordinates and multiple second maximum ordinates corresponding to the multiple left lung contour lines and the multiple right lung contour lines; determining multiple first abscissas corresponding to the multiple first maximum ordinates based on the multiple first maximum ordinates and the multiple left lung contour lines; determining multiple second abscissas corresponding to the multiple second maximum ordinates based on the multiple second maximum ordinates and the multiple right lung contour lines; configuring the multiple first maximum ordinates and their corresponding multiple first abscissas as multiple left lung vertices; and configuring the multiple second maximum ordinates and their corresponding multiple second abscissas as multiple right lung vertices.

[0213] In embodiments of this disclosure and other possible embodiments, the method for determining multiple first maximum ordinates and multiple second maximum ordinates corresponding to the multiple left lung contours and the multiple right lung contours includes: establishing a coordinate system corresponding to the two-dimensional DR left lung image and the two-dimensional DR right lung image at multiple moments during the breathing process; and determining multiple first maximum ordinates and multiple second maximum ordinates corresponding to the multiple left lung contours and the multiple right lung contours in the coordinate system.

[0214] In embodiments of this disclosure, the method for determining corresponding sequences of multiple left-lung diaphragm motion points and multiple right-lung diaphragm motion points based on the multiple left-lung contour lines and the multiple right-lung contour lines includes: detecting multiple lowest points of the left lung and multiple lowest points of the right lung corresponding to the multiple left-lung contour lines and the multiple right-lung contour lines, and configuring the multiple lowest points of the left lung and the multiple lowest points of the right lung as the starting points of the multiple left-lung diaphragm motion point sequences and the multiple right-lung diaphragm motion point sequences, respectively; detecting the multiple lowest points of the left lung and the multiple right-lung contour lines corresponding to the multiple left-lung contour lines and the multiple right-lung contour lines respectively. The system identifies multiple left lung vertices and multiple right lung vertices, and configures the coordinates corresponding to the multiple left lung vertices and the multiple right lung vertices as candidate endpoints for the multiple left lung diaphragm motion point sequences and the multiple right lung diaphragm motion point sequences, respectively. The system determines the multiple left lung diaphragm motion point sequences based on the starting points of the multiple left lung diaphragm motion point sequences and the candidate endpoints of the multiple left lung diaphragm motion point sequences, respectively. The system also determines the multiple right lung diaphragm motion point sequences based on the starting points of the multiple right lung diaphragm motion point sequences and the candidate endpoints of the multiple right lung diaphragm motion point sequences, respectively.

[0215] Similarly, in embodiments of this disclosure and other possible embodiments, in the coordinate system, the plurality of left lung diaphragm motion point sequences are determined based on the starting point of the plurality of left lung diaphragm motion point sequences and the candidate ending point of the plurality of left lung diaphragm motion point sequences, respectively.

[0216] In embodiments of this disclosure, the method for determining the plurality of left lung diaphragm motion point sequences based on the starting points and candidate endpoints of the plurality of left lung diaphragm motion point sequences includes: obtaining a first set step size; calculating, based on the first set step size, a plurality of first slope values ​​corresponding to the points from the starting points to the candidate endpoints of the plurality of left lung diaphragm motion point sequences; determining the endpoints of the plurality of left lung diaphragm motion point sequences based on the plurality of first slope values; or, determining, respectively, a left lung contour line between the starting points and the candidate endpoints of the plurality of left lung diaphragm motion point sequences; and, on the left lung contour line between them, determining the endpoints of the plurality of left lung diaphragm motion point sequences in an interactive manner.

[0217] In the embodiments of this disclosure and other possible embodiments, those skilled in the art can configure the first set step size according to actual needs. For example, the first set step size can be configured to any value between 1 and 10 or other values.

[0218] For example, in the first starting point (x1, y1) of multiple left lung diaphragm motion point sequences, if the first set step size is configured as n, then the first slope values ​​corresponding to the multiple first slope values ​​are respectively (y1, y1, y2, y3, y4 ... 1+n -y1) / [x 1+n -x1), (y 1+2*n -y 1+n ) / [x 1+2*n -x 1+n ), ...

[0219] In an embodiment of this disclosure, the method for determining the endpoint of the plurality of left lung diaphragm motion point sequences based on the plurality of first slope values ​​includes: determining the candidate endpoint corresponding to the change of the sign of the plurality of first slope values ​​from negative to positive as the endpoint of the plurality of left lung diaphragm motion point sequences.

[0220] In embodiments of this disclosure and other possible embodiments, the method for interactively determining the endpoints of the plurality of left lung diaphragm motion point sequences along the left lung contour line includes:

[0221] The multiple left lung contour lines are configured with colors respectively, and the multiple left lung contour lines after the color configuration are displayed respectively;

[0222] On the multiple left lung contour lines shown, click the endpoints of the multiple left lung diaphragm motion point sequences to determine the endpoints of the multiple left lung diaphragm motion point sequences.

[0223] In embodiments of this disclosure, the method for determining the plurality of right lung diaphragm motion point sequences based on the starting points and candidate ending points of the plurality of right lung diaphragm motion point sequences includes: obtaining a second set step size; calculating a plurality of second slope values ​​corresponding to the starting points and candidate ending points of the plurality of right lung diaphragm motion point sequences based on the second set step size; determining the ending points of the plurality of right lung diaphragm motion point sequences based on the plurality of second slope values; or, determining a right lung contour line between the starting points and candidate ending points of the plurality of right lung diaphragm motion point sequences; and determining the ending points of the plurality of right lung diaphragm motion point sequences in an interactive manner along the right lung contour line between them.

[0224] In the embodiments of this disclosure and other possible embodiments, those skilled in the art can configure the second set step size according to actual needs. For example, the second set step size can be configured as any value between 1 and 10 or other values.

[0225] For example, in the first starting point (x2, y2) of multiple right lung diaphragm motion point sequences, if the second set step size is configured as m, then the corresponding second slope values ​​among the multiple second slope values ​​are (y2, ... 2+m -y2) / [x 2+m -x2), (y 2+2*m -y 2+m ) / [x 2+2*m -x 2+m ), ...

[0226] In an embodiment of this disclosure, the method for determining the endpoint of the plurality of right lung diaphragm motion point sequences based on the plurality of second slope values ​​includes: determining the candidate endpoints corresponding to the change of the signs of the plurality of second slope values ​​from positive to negative as the endpoints of the plurality of right lung diaphragm motion point sequences.

[0227] In embodiments of this disclosure and other possible embodiments, the method for interactively determining the endpoints of the plurality of right lung diaphragm motion point sequences on the right lung contour lines includes: configuring the colors of the plurality of right lung contour lines respectively, and displaying the plurality of right lung contour lines after the color configuration respectively; clicking the endpoints of the plurality of right lung diaphragm motion point sequences on the displayed plurality of right lung contour lines respectively, thereby determining the endpoints of the plurality of right lung diaphragm motion point sequences respectively.

[0228] Step S104: Determine the movement of the left lung diaphragm based on the multiple left lung movement vertices and the multiple left lung diaphragm movement point sequences, and determine the movement of the right lung diaphragm based on the multiple right lung movement vertices and the multiple right lung diaphragm movement point sequences.

[0229] In embodiments of this disclosure and other possible embodiments, the method for determining the motion of the left lung diaphragm based on the plurality of left lung motion vertices and the plurality of left lung diaphragm motion point sequences includes: configuring the plurality of left lung motion vertices as left lung motion reference points respectively; calculating a plurality of first distances between each motion point in the plurality of left lung diaphragm motion point sequences and the left lung motion reference points based on the left lung motion reference points; or, displaying the plurality of left lung motion vertices with lines of a first color and displaying the plurality of left lung diaphragm motion point sequences with lines of a second color respectively.

[0230] In embodiments of this disclosure and other possible embodiments, the method for determining the motion of the right lung diaphragm based on the plurality of right lung motion vertices and the plurality of right lung diaphragm motion point sequences includes: configuring the plurality of right lung motion vertices as right lung motion reference points respectively; calculating a plurality of second distances between each motion point in the plurality of right lung diaphragm motion point sequences and the right lung motion reference points based on the right lung motion reference points; or, displaying the plurality of right lung motion vertices with lines of a first color and the plurality of right lung diaphragm motion point sequences with lines of a second color respectively.

[0231] In embodiments of this disclosure and other possible embodiments, the method of displaying the plurality of left lung motion vertices and the plurality of right lung motion vertices with a first-colored line, and displaying the plurality of left lung diaphragm motion point sequences and the plurality of right lung diaphragm motion point sequences with a second-colored line, includes: acquiring DR left lung images and DR right lung images corresponding to DR images at multiple moments during the breathing process; displaying the DR left lung images and DR right lung images corresponding to DR images at multiple moments during the breathing process; and displaying the plurality of left lung motion vertices and the plurality of right lung motion vertices with a first-colored line, and displaying the plurality of left lung diaphragm motion point sequences and the plurality of right lung diaphragm motion point sequences with a second-colored line on the DR left lung images and DR right lung images corresponding to DR images at multiple moments during the breathing process.

[0232] In embodiments of this disclosure and other possible embodiments, the method of displaying multiple left lung diaphragm motion point sequences and multiple right lung diaphragm motion point sequences with a second-colored line further includes: constructing multiple left lung diaphragm motion curves corresponding to the multiple left lung diaphragm motion point sequences, and configuring the motion point with the smallest slope of each left lung diaphragm motion curve in the multiple left lung diaphragm motion curves as the left lung diaphragm motion point to be displayed at multiple moments during the breathing process; displaying the left lung diaphragm motion point to be displayed with the second-colored line; constructing multiple right lung diaphragm motion curves corresponding to the multiple right lung diaphragm motion point sequences, and configuring the motion point with the smallest slope of each right lung diaphragm motion curve in the multiple right lung diaphragm motion curves as the right lung diaphragm motion point to be displayed at multiple moments during the breathing process; and displaying the right lung diaphragm motion point to be displayed with the second-colored line.

[0233] In embodiments of this disclosure and other possible embodiments, a method for correcting the left lung diaphragm movement points to be displayed at multiple moments during the breathing process includes: acquiring multiple left lung areas during the breathing process; calculating multiple first coordinate points corresponding to a first predetermined slope value of each left lung diaphragm movement curve in the multiple left lung diaphragm movement curves; sorting the multiple first coordinate points corresponding to the first predetermined slope value of each left lung diaphragm movement curve from smallest to largest; and correcting the left lung diaphragm movement points to be displayed at multiple moments based on the multiple left lung areas and the sorted multiple first coordinate points.

[0234] In the embodiments disclosed herein and other possible embodiments, those skilled in the art can configure the first set slope value according to actual needs. For example, the first set slope value can be configured as any value between -0.2 and 0 or other values.

[0235] In embodiments of this disclosure and other possible embodiments, the method for correcting the left lung diaphragm movement points to be displayed at multiple time moments based on the plurality of left lung areas and the plurality of sorted first coordinate points includes: determining the plurality of sorted first coordinate points corresponding to the plurality of left lung areas from the largest area to the smallest area; wherein, the ordinate values ​​of the plurality of sorted first coordinate points corresponding to the plurality of left lung areas from the largest area to the smallest area increase sequentially; if the ordinate values ​​of the left lung diaphragm movement points to be displayed at multiple time moments corresponding to the plurality of left lung areas from the largest area to the smallest area increase sequentially, then the left lung diaphragm movement points to be displayed at multiple time moments are not corrected; otherwise, the non-increasing movement points among the left lung diaphragm movement points to be displayed at multiple time moments are corrected.

[0236] In the embodiments disclosed herein and other possible embodiments, the method for correcting non-increasing motion points among the multiple time-series left lung diaphragm motion points to be displayed includes: extracting the first ordinate corresponding to the non-increasing motion point among the multiple time-series left lung diaphragm motion points to be displayed, and the second and third ordinates corresponding to two adjacent time-series left lung diaphragm motion points on both sides of the non-increasing motion point; determining, among the sorted multiple first coordinate points, a first ordinate to be configured corresponding to a coordinate greater than the third ordinate and less than the second ordinate (or, greater than the second ordinate and less than the third ordinate); and configuring the first ordinate to be configured and its corresponding abscissa as the non-increasing motion point among the multiple time-series left lung diaphragm motion points to be displayed.

[0237] In embodiments of this disclosure and other possible embodiments, a method for correcting the right lung diaphragm movement points to be displayed at multiple moments during the breathing process includes: acquiring multiple right lung areas during the breathing process; calculating multiple second coordinate points corresponding to a second predetermined slope value of each right lung diaphragm movement curve in the multiple right lung diaphragm movement curves; sorting the multiple second coordinate points corresponding to the second predetermined slope value of each right lung diaphragm movement curve in ascending order of their ordinate; and correcting the multiple right lung diaphragm movement points to be displayed at multiple moments based on the multiple right lung areas and the sorted multiple second coordinate points.

[0238] In the embodiments disclosed herein and other possible embodiments, those skilled in the art can configure the second set slope value according to actual needs. For example, the second set slope value can be configured as any value between 0 and 0.2 or other values.

[0239] In embodiments of this disclosure and other possible embodiments, the method for correcting the right lung diaphragm motion points to be displayed at multiple time moments based on the plurality of right lung areas and the plurality of sorted second coordinate points includes: determining the plurality of sorted second coordinate points corresponding to the plurality of right lung areas from the largest area to the smallest area; wherein, the ordinate values ​​of the plurality of sorted second coordinate points corresponding to the plurality of right lung areas from the largest area to the smallest area increase sequentially; if the ordinate values ​​of the plurality of sorted second coordinate points corresponding to the plurality of right lung areas from the largest area to the smallest area increase sequentially with the ordinate values ​​of the right lung diaphragm motion points to be displayed at multiple time moments, then no correction is made to the right lung diaphragm motion points to be displayed at multiple time moments; otherwise, the non-increasing motion points among the right lung diaphragm motion points to be displayed at multiple time moments are corrected.

[0240] In the embodiments disclosed herein and other possible embodiments, the method for correcting non-increasing motion points among the multiple time-series right lung diaphragm motion points to be displayed includes: extracting the first ordinate corresponding to the non-increasing motion point among the multiple time-series right lung diaphragm motion points to be displayed, and the second and third ordinates corresponding to two adjacent time-series right lung diaphragm motion points on both sides of the non-increasing motion point; determining, among the sorted multiple first coordinate points, a second ordinate to be configured corresponding to a coordinate greater than the third ordinate and less than the second ordinate (or, greater than the second ordinate and less than the third ordinate); and configuring the second ordinate to be configured and its corresponding abscissa as the non-increasing motion point among the multiple time-series right lung diaphragm motion points to be displayed.

[0241] In embodiments of this disclosure and other possible embodiments, the method of displaying the plurality of left lung motion vertices and the plurality of right lung motion vertices with a first-colored line, and displaying the plurality of left lung diaphragm motion point sequences and the plurality of right lung diaphragm motion point sequences with a second-colored line, further includes: obtaining a first configuration parameter corresponding to the first-colored line; wherein the first configuration parameter includes: a first line length and a first line width; displaying the plurality of left lung motion vertices and the plurality of right lung motion vertices with the first configuration parameter configured for the first-colored line; obtaining a first configuration parameter corresponding to the second-colored line; wherein the second configuration parameter includes: a second line length and a second line width; displaying the plurality of left lung diaphragm motion point sequences and the plurality of right lung diaphragm motion point sequences with the second configuration parameter configured for the second-colored line. The first color is configured as red and the second color is configured as blue. Alternatively, the first color configuration and the second color configuration can be configured as lines of the same color, such as red, blue, or other colors.

[0242] In the embodiments of this disclosure and other possible embodiments, the method further includes: using the above-described diaphragm movement detection method to detect the diaphragm movement trajectory corresponding to the patient to be evaluated, including: obtaining a set movement trajectory corresponding to a healthy person; and assessing whether there is a functional disorder in the diaphragm movement of the patient to be evaluated based on the diaphragm movement trajectory corresponding to the patient to be evaluated and the set movement trajectory.

[0243] In embodiments of this disclosure and other possible embodiments, the method for determining the diaphragm movement trajectory corresponding to the patient to be evaluated and the set movement trajectory includes: drawing a first diaphragm movement trajectory corresponding to a plurality of left lung diaphragm movement point sequences corresponding to the patient to be evaluated; and / or drawing a second diaphragm movement trajectory corresponding to a plurality of right lung diaphragm movement point sequences corresponding to the patient to be evaluated; and then determining the diaphragm movement trajectory corresponding to the patient to be evaluated and the set movement trajectory.

[0244] In the embodiments of this disclosure and other possible embodiments, the method for determining the set motion trajectory corresponding to the healthy person before obtaining the set motion trajectory corresponding to the healthy person includes: obtaining a preset number of multiple diaphragm motion trajectories corresponding to the healthy person.

[0245] By fitting the multiple diaphragm movement trajectories, the set movement trajectory corresponding to the healthy person is obtained.

[0246] Similarly, in the embodiments of this disclosure and other possible embodiments, the preset motion trajectory corresponding to the healthy person includes: drawing a first preset motion trajectory corresponding to the left lung and / or a second preset motion trajectory corresponding to the right lung of the healthy person.

[0247] In embodiments of this disclosure and other possible embodiments, before obtaining the set motion trajectory corresponding to a healthy person, the method for determining the set motion trajectory corresponding to the healthy person includes: obtaining a preset number of left lung diaphragm motion trajectories and right lung diaphragm motion trajectories corresponding to the healthy person; fitting the multiple left lung diaphragm motion trajectories to obtain a first set motion trajectory corresponding to the left lung of the healthy person; and fitting the multiple right diaphragm motion trajectories to obtain a second set motion trajectory corresponding to the left lung of the healthy person.

[0248] In the embodiments of this disclosure and other possible embodiments, those skilled in the art can configure the preset number according to actual needs. Meanwhile, the similarity calculation method can be configured as one or more of the following: cosine similarity, adjusted cosine similarity, Pearson correlation coefficient, Jaccard similarity coefficient, Tanimoto coefficient (generalized Jaccard similarity coefficient), log-likelihood similarity / log-likelihood similarity rate, mutual information / information gain, relative entropy / KL divergence, and information retrieval-term frequency-inverse document frequency (TF-IDF).

[0249] In embodiments of this disclosure and other possible embodiments, the method for assessing whether there is a functional disorder in the diaphragm movement of the patient to be assessed based on the diaphragm movement trajectory corresponding to the patient to be assessed and the set movement trajectory includes: assessing whether there is a functional disorder in the left lung diaphragm movement of the patient to be assessed based on the first diaphragm movement trajectory and the first set movement trajectory; and / or, assessing whether there is a functional disorder in the right lung diaphragm movement of the patient to be assessed based on the second diaphragm movement trajectory and the second set movement trajectory.

[0250] In embodiments of this disclosure, the method for assessing whether there is a functional disorder in the diaphragm movement of the patient to be assessed based on the diaphragm movement trajectory corresponding to the patient to be assessed and the set movement trajectory includes: calculating the similarity between the diaphragm movement trajectory and the set movement trajectory; if the similarity is less than the set similarity, then the diaphragm movement of the patient to be assessed has a functional disorder; otherwise, the diaphragm movement of the patient to be assessed does not have a functional disorder.

[0251] In the embodiments of this disclosure and other possible embodiments, those skilled in the art can configure the similarity of the set motion trajectory according to actual needs. For example, the similarity of the set motion trajectory can be configured to 0.8 or other values.

[0252] In embodiments of this disclosure and other possible embodiments, the method of calculating the similarity between the diaphragm movement trajectory and the set movement trajectory; if the similarity is less than the set similarity, then the diaphragm movement of the patient to be evaluated has a functional disorder; otherwise, the diaphragm movement of the patient to be evaluated does not have a functional disorder, includes: calculating a first similarity between the first diaphragm movement trajectory and the first set movement trajectory; if the first similarity is less than the set similarity, then the left lung diaphragm movement of the patient to be evaluated has a functional disorder; otherwise, the left lung diaphragm movement of the patient to be evaluated does not have a functional disorder; and / or, calculating a second similarity between the second diaphragm movement trajectory and the second set movement trajectory; if the second similarity is less than the set similarity, then the right lung diaphragm movement of the patient to be evaluated has a functional disorder; otherwise, the right lung diaphragm movement of the patient to be evaluated does not have a functional disorder.

[0253] The entity executing the method for determining lung field area and assessing lung development in DR images can be a lung field area determination and lung development assessment device. For example, the method can be executed by a terminal device, server, or other processing device. 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 method for determining lung field area and assessing lung development in DR images can be implemented by a processor calling computer-readable instructions stored in memory.

[0254] Those skilled in the art will understand that in the above-described method for determining the lung field area in DR 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.

[0255] Figure 3 A block diagram of a lung field area determination device according to an embodiment of the present disclosure is shown, such as Figure 3 As shown, the lung field area determination device includes: a first acquisition unit 101, used to acquire two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple moments during the breathing process; and a determination unit 102, used to determine the left lung area and / or right lung area during the breathing process based on the two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple moments during the breathing process.

[0256] In embodiments of this disclosure, the determining unit 102 includes: a set area acquisition unit, used to acquire a set area corresponding to a single pixel; a statistics unit, used to count the number of left lung pixels in the two-dimensional DR left lung image and / or the number of right lung pixels in the two-dimensional DR right lung image at multiple moments during the breathing process; and an area calculation unit, used to calculate the left lung area and / or right lung area corresponding to the number of left lung pixels in the two-dimensional DR left lung image and / or the number of right lung pixels in the two-dimensional DR right lung image at multiple moments during the breathing process based on the set area.

[0257] In embodiments of this disclosure, the area calculation unit includes: a left lung area calculation unit and / or a right lung area calculation unit; the left lung area calculation unit is used to multiply the set area by the number of left lung pixels in the two-dimensional DR left lung images at multiple moments during the breathing process to obtain the corresponding left lung area during the breathing process; and / or, the right lung area calculation unit is used to multiply the set area by the number of right lung pixels in the two-dimensional DR right lung images at multiple moments during the breathing process to obtain the corresponding right lung area during the breathing process.

[0258] In embodiments of this disclosure, the system further includes a segmentation unit; the segmentation unit is configured to acquire two-dimensional DR lung images at multiple moments during the breathing process before acquiring two-dimensional DR left lung images and / or two-dimensional DR right lung images at multiple moments during the breathing process, and to segment the two-dimensional DR lung images at multiple moments during the breathing process into left and right lungs to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple moments.

[0259] In embodiments of this disclosure, the segmentation unit includes: a detection unit; the detection unit is used to detect the costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edges of the left and right chest images of the two-dimensional DR lung images at multiple time points during the breathing process, respectively, to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points; or, the segmentation unit includes: a model and data acquisition unit, a training unit, and an output unit; the model and data acquisition unit is used to acquire a segmentation model of a preset convolutional neural network and DR lung region label images for training the segmentation model; the training unit is used to train the segmentation model using the DR lung region label images used to train the segmentation model; the output unit is used to complete the segmentation of the left and right lungs of the two-dimensional DR lung images at multiple time points during the breathing process based on the trained segmentation model, to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points.

[0260] In embodiments of this disclosure, the segmentation unit further includes a label determination unit; the label determination unit is used to perform costal margin boundary, lung apex boundary, and mediastinal and transverse diaphragmatic edge detection on the left and right chest images of multiple DR lung area images respectively, to obtain DR lung area label images corresponding to the multiple DR lung area images.

[0261] Figure 4 A block diagram of a lung development assessment device according to an embodiment of the present disclosure is shown, such as Figure 4As shown, the lung development assessment device includes: the left lung area and / or right lung area obtained during the breathing process using the lung field area determination method or device described above; and a second acquisition unit 201, used to acquire the patient's age, height, and gender information corresponding to the left lung area and / or right lung area during the breathing process, and determine the set left lung field area and set right lung field area during the breathing process based on the age, height, and gender information; and a first assessment unit 202, used to assess the patient's lung development based on the left lung field area, right lung field area, set left lung field area, and set right lung field area during the patient's breathing process.

[0262] In embodiments of this disclosure, the first assessment unit further includes: a left lung assessment unit and a right lung assessment unit; the left lung assessment unit is configured to determine if the left lung field area during the breathing process is smaller than a corresponding set left lung field area during the breathing process, in which case the patient's left lung development is abnormal; otherwise, the patient's left lung development is normal; or, the left lung assessment unit is configured to calculate the average left lung area corresponding to the left lung field area during the breathing process and calculate the set average left lung area corresponding to the set left lung field area during the breathing process; if the average left lung area is smaller than the set average left lung area, in which case the patient's left lung development is abnormal; otherwise, the patient's left lung development is normal.

[0263] In embodiments of this disclosure, the right lung assessment unit is configured to determine if the right lung field area during breathing is smaller than a corresponding set right lung field area during breathing, indicating that the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal. Alternatively, the right lung assessment unit is configured to calculate the average right lung area corresponding to the right lung field area during breathing and the set average right lung area corresponding to the set right lung field area during breathing, respectively; if the average right lung area is smaller than the set average right lung area, the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal.

[0264] In embodiments of this disclosure, the system further includes: a condition discrimination unit; the condition discrimination unit is configured to, under the condition that the lung development assessment of the patient is based on the left lung field area, right lung field area, a set left lung field area, and a set right lung field area during the patient's breathing process, and the result is that the right lung development is normal and / or the right lung development is normal, a second acquisition unit is configured to acquire the left lung area and / or right lung area corresponding to the patient's deep inspiration and deep expiration during the breathing process, and determine the corresponding set value of the left lung area and / or the set value of the right lung area based on the age information, height information, and gender information; and to perform a lung development assessment of the patient based on the left lung area and the set value of the left lung area during deep inspiration and deep expiration and / or based on the right lung area and the set value of the right lung area.

[0265] In embodiments of this disclosure, the second assessment unit includes: a second left lung assessment unit; the second left lung assessment unit is used to assess the lung development of the patient based on the left lung area and a set value for the left lung area, and includes: a first calculation unit and a first comparison unit; wherein, the first calculation unit is used to calculate a first difference between the left lung area under deep inspiration and the left lung area under deep expiration; wherein, the first comparison unit is used to assess the lung development of the patient based on the first difference and the set value for the left lung area.

[0266] In an embodiment of this disclosure, the first comparison unit assesses the patient's lung development based on the first difference and a set value for the left lung area, including: if the first difference is greater than or equal to the set value for the left lung area, the patient's left lung development is abnormal; otherwise, the patient's left lung development is normal.

[0267] In embodiments of this disclosure, the second assessment unit further includes: a second right lung assessment unit; the second right lung assessment unit is used to assess the lung development of the patient based on the right lung area and a set value for the right lung area, including: a second calculation unit and a second comparison unit; wherein, the second calculation unit is used to calculate a second difference between the right lung area under deep inspiration and the right lung area under deep expiration; the second comparison unit is used to assess the lung development of the patient based on the second difference and the set value for the right lung area.

[0268] In an embodiment of this disclosure, the second comparison unit is used to assess the patient's lung development based on the second difference and a set value for the right lung area, including: if the second difference is greater than or equal to the set value for the right lung area, then the patient's right lung development is abnormal; otherwise, the patient's right lung development is normal.

[0269] In some embodiments, the functions or modules of the apparatus provided in this disclosure can be used to perform the DR image lung field area determination and lung development assessment methods described in the above method embodiments. The specific implementation can be referred to the description of the above lung field area determination and lung development assessment method embodiments, which will not be repeated here for the sake of brevity.

[0270] This disclosure also proposes a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the aforementioned methods for determining lung field area and assessing lung development in DR images. The computer-readable storage medium may be a non-volatile computer-readable storage medium.

[0271] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured for the aforementioned method for determining lung field area and assessing lung development in DR images. The electronic device may be provided as a terminal, a server, or other form of device.

[0272] Figure 5 This 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.

[0273] Reference Figure 5 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.

[0274] 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.

[0275] 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.

[0276] 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.

[0277] 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). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0278] 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 electronic device 800 is in an operating mode, such as call mode, recording mode, and 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.

[0279] 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.

[0280] 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.

[0281] 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.

[0282] 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.

[0283] 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.

[0284] Figure 6 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 6The 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.

[0285] 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.

[0286] 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.

[0287] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0288] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0289] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0290] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0291] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0292] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0293] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0294] 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.

[0295] 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 lung field area determination method characterized by, The method comprises the following steps: acquiring two-dimensional DR left lung images at multiple moments in a breathing process; wherein, before acquiring the two-dimensional DR left lung images at multiple moments in the breathing process, chest detection is performed on a to-be-processed DR image, and information outside the chest in the to-be-processed DR image is removed, comprising: calculating a plurality of first gradient amplitudes corresponding to each pixel point in the to-be-processed DR image in the horizontal direction and a plurality of second gradient amplitudes corresponding to each pixel point in the vertical direction respectively; determining a plurality of total gradient amplitudes based on the plurality of first gradient amplitudes and the plurality of second gradient amplitudes; integrating the plurality of first gradient amplitudes corresponding to the horizontal direction and the plurality of second gradient amplitudes in the vertical direction along the direction perpendicular thereto to obtain a plurality of first integral values and a plurality of second integral values; integrating the plurality of total gradient amplitudes in the vertical direction and the horizontal direction to obtain a plurality of third integral values and a plurality of fourth integral values; calculating a plurality of first local maximum values corresponding to a plurality of first ratios between the plurality of first integral values and the corresponding plurality of third integral values; determining a plurality of second ratios between the plurality of second integral values and the corresponding plurality of fourth integral values; calculating a plurality of first local minimum values and a plurality of second local minimum values corresponding to the plurality of first ratios and the plurality of second ratios; configuring position information corresponding to the minimum value of the plurality of second local minimum values as a first segmentation position of the neck or the shoulder; determining the maximum value of the plurality of first local maximum values on one side of the center line of the to-be-processed DR image and the minimum value of the plurality of first local minimum values on the other side of the center line; configuring position information corresponding to the maximum value as a thoracic side of a second segmentation position; configuring position information corresponding to the minimum value as the other side of the thoracic of the second segmentation position; determining the left lung area in the breathing process based on the two-dimensional DR left lung images at multiple moments in the breathing process.

2. The lung field area determination method of claim 1, wherein, The method for determining the left lung area in the breathing process based on the two-dimensional DR left lung images at multiple moments in the breathing process comprises the following steps: acquiring a set area corresponding to a single pixel; respectively counting the number of left lung pixels in the two-dimensional DR left lung images at multiple moments in the breathing process; respectively calculating the left lung area corresponding to the number of left lung pixels in the two-dimensional DR left lung images at multiple moments in the breathing process based on the set area.

3. The lung field area determination method of claim 2, wherein, The method for respectively calculating the left lung area corresponding to the number of left lung pixels in the two-dimensional DR left lung images at multiple moments in the breathing process based on the set area comprises the following steps: respectively multiplying the set area by the number of left lung pixels in the two-dimensional DR left lung images at multiple moments in the breathing process to obtain the corresponding left lung area in the breathing process.

4. The lung field area determination method according to any one of claims 1 to 3, characterized in that, Before acquiring the two-dimensional DR left lung images at multiple moments in the breathing process, the method comprises the following steps: acquiring two-dimensional DR lung images at multiple moments in the breathing process; performing left lung and right lung segmentation on the two-dimensional DR lung images at multiple moments in the breathing process to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple moments.

5. The lung field area determination method of claim 4, wherein, The two-dimensional DR lung images at multiple moments in the breathing process are segmented into left lung and right lung to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple moments, including: The left and right chest images of the two-dimensional DR lung images at multiple moments in the breathing process are respectively subjected to rib boundary, lung apex boundary and mediastinum and diaphragm edge detection to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple moments.

6. The lung field area determination method of claim 4, wherein, The two-dimensional DR lung images at multiple moments in the breathing process are segmented into left lung and right lung to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple moments, including: A segmentation model of a preset convolutional neural network and a DR lung region label image used for training the segmentation model are obtained; The segmentation model is trained by using the DR lung region label image used for training the segmentation model; Based on the trained segmentation model, the two-dimensional DR lung images at multiple moments in the breathing process are segmented into left lung and right lung to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple moments.

7. The lung field area determination method of claim 6, wherein, Determining the DR lung region label image used for training the segmentation model includes: The left and right chest images of multiple DR lung region images are respectively subjected to rib boundary, lung apex boundary and mediastinum and diaphragm edge detection to obtain DR lung region label images corresponding to the multiple DR lung region images.

8. A lung field area determination method characterized by, It includes: Obtain two-dimensional DR right lung images at multiple moments in the breathing process; Before obtaining the two-dimensional DR right lung images at multiple moments in the breathing process, the chest cavity of a to-be-processed DR image is detected to remove information outside the chest cavity in the to-be-processed DR image, including: calculating a plurality of first gradient amplitudes corresponding to each pixel point in the to-be-processed DR image in the horizontal direction and a plurality of second gradient amplitudes corresponding to each pixel point in the vertical direction; based on the plurality of first gradient amplitudes and the plurality of second gradient amplitudes, a plurality of total gradient amplitudes are determined; the plurality of first gradient amplitudes and the plurality of second gradient amplitudes are integrated along the direction perpendicular to the direction, to obtain a plurality of first integral values and a plurality of second integral values; the plurality of total gradient amplitudes are integrated in the vertical direction and the horizontal direction to obtain a plurality of third integral values and a plurality of fourth integral values; a plurality of first local maxima corresponding to a plurality of first ratios between the plurality of first integral values and the corresponding plurality of third integral values; a plurality of first local minima and a plurality of second local minima corresponding to a plurality of second ratios between the plurality of first ratios and the corresponding plurality of fourth integral values; the position information corresponding to the minimum value of the plurality of second local minima is configured as the first segmentation position of the neck or shoulder; according to the center line of the to-be-processed DR image, the maximum value of the plurality of first local maxima on one side of the center line and the minimum value of the plurality of first local minima on the other side of the center line are determined; the position information corresponding to the maximum value is configured as the second segmentation position of one side of the thoracic cage; the position information corresponding to the minimum value is configured as the second segmentation position of the other side of the thoracic cage; Determine the right lung area in the breathing process based on the two-dimensional DR right lung images at multiple time points in the breathing process.

9. The lung field area determination method of claim 8, wherein, The determination of the right lung area in the breathing process based on the two-dimensional DR right lung images at multiple time points in the breathing process comprises: Obtain the set area corresponding to a single pixel; Respectively count the number of right lung pixels in the two-dimensional DR right lung images at multiple time points in the breathing process; Based on the set area, respectively calculate the right lung area corresponding to the number of right lung pixels in the right lung image of the two-dimensional DR right lung image at multiple time points in the breathing process.

10. The lung field area determination method of claim 9, wherein, The calculation of the right lung area corresponding to the number of right lung pixels in the right lung image of the two-dimensional DR right lung image at multiple time points in the breathing process based on the set area comprises: Respectively multiply the set area by the number of right lung pixels in the two-dimensional DR right lung image at multiple time points in the breathing process to obtain the corresponding right lung area in the breathing process.

11. The lung field area determination method according to any one of claims 8 to 10, characterized in that, Before obtaining the two-dimensional DR right lung images at multiple time points in the breathing process, obtain the two-dimensional DR lung images at multiple time points in the breathing process, and perform left lung and right lung segmentation on the two-dimensional DR lung images at multiple time points in the breathing process to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points.

12. The lung field area determination method of claim 11, wherein, The left lung and right lung segmentation of the two-dimensional DR lung images at multiple time points in the breathing process to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points comprises: Respectively detect the rib edge boundary, lung tip boundary, and mediastinum and diaphragm edge of the left and right chest images of the two-dimensional DR lung images at multiple time points in the breathing process to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points.

13. The lung field area determination method of claim 11, wherein, The left lung and right lung segmentation of the two-dimensional DR lung images at multiple time points in the breathing process to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points comprises: Obtain the segmentation model of a preset convolutional neural network and the DR lung region label image used to train the segmentation model; Train the segmentation model using the DR lung region label image used to train the segmentation model; Based on the trained segmentation model, complete the left lung and right lung segmentation of the two-dimensional DR lung images at multiple time points in the breathing process to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points.

14. The lung field area determination method of claim 13, wherein, The determination of the right lung area in the breathing process based on the two-dimensional DR right lung images at multiple time points in the breathing process comprises:

15. A method of lung development assessment, comprising: Respectively detect the rib edge boundary, lung tip boundary, and mediastinum and diaphragm edge of the left and right chest images of the two-dimensional DR lung images at multiple time points in the breathing process to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points. Comprise: Apply the left lung area or right lung area obtained by the determination method of any one of claims 1-14; Obtain the age information, height information, and gender information of the patient corresponding to the left lung area or right lung area in the breathing process; Determine the set left lung area and set right lung area in the breathing process according to the age information, height information, and gender information; Respectively evaluate the lung development of the patient according to the left lung area, right lung area, set left lung area, and set right lung area in the breathing process of the patient.

16. The method of claim 15, wherein the lung development assessment is performed on a subject having a lung disease. The lung development evaluation of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area in the breathing process of the patient respectively, comprises: If the left lung area in the breathing process is less than the corresponding set left lung area in the breathing process, the left lung of the patient is abnormal; otherwise, the left lung of the patient is normal; or, the left lung average area corresponding to the left lung area in the breathing process is calculated and the set left lung average area corresponding to the set left lung area in the breathing process is calculated; if the left lung average area is less than the set left lung average area, the left lung of the patient is abnormal; otherwise, the left lung of the patient is normal. The lung development evaluation of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area in the breathing process of the patient respectively, comprises:

17. The method of lung development assessment according to any one of claims 15 or 16, wherein, If the right lung area in the breathing process is less than the corresponding set right lung area in the breathing process, the right lung of the patient is abnormal; otherwise, the right lung of the patient is normal; or, the right lung average area corresponding to the right lung area in the breathing process is calculated and the set right lung average area corresponding to the set right lung area in the breathing process is calculated; if the right lung average area is less than the set right lung average area, the right lung of the patient is abnormal; otherwise, the right lung of the patient is normal. Further comprising:

18. The method of lung development assessment according to any one of claims 15 or 16, wherein, Under the condition that the result of the lung development evaluation of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area in the breathing process of the patient respectively is that the left lung is normal, The left lung area corresponding to the deep inspiration and the deep expiration of the patient in the breathing process is obtained, and the corresponding left lung area set value and / or right lung area set value is determined according to the age information, height information and gender information; The lung development evaluation of the patient is performed based on the left lung area under the deep inspiration and the deep expiration and the left lung area set value. Further comprising:

19. The method of claim 17, wherein the lung development assessment is performed on a subject having a lung disease. Under the condition that the result of the lung development evaluation of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area in the breathing process of the patient respectively is that the right lung is normal, The left lung area corresponding to the deep inspiration and the deep expiration of the patient in the breathing process is obtained, and the corresponding left lung area set value and / or right lung area set value is determined according to the age information, height information and gender information; The lung development evaluation of the patient is performed based on the left lung area under the deep inspiration and the deep expiration and the left lung area set value. The lung development evaluation of the patient based on the left lung area under the deep inspiration and the deep expiration and the left lung area set value, comprises:

20. The method of claim 18, wherein the lung development assessment is performed on a subject having a lung disease. The first difference value of the left lung area under the deep inspiration and the left lung area under the deep expiration is calculated; The first difference value and the left lung area set value are used for the lung development evaluation of the patient. The lung development evaluation of the patient based on the left lung area under the deep inspiration and the deep expiration and the left lung area set value, comprises:

21. The method of claim 19, wherein the lung development assessment is performed on a subject having a lung disease. The first difference value of the left lung area under the deep inspiration and the left lung area under the deep expiration is calculated; ​ The first difference value and the left lung area set value are used to evaluate lung development of the patient.

22. The method of lung development assessment according to any one of claims 20 or 21, wherein, The first difference value and the left lung area set value are used to evaluate lung development of the patient, including: If the first difference value is greater than or equal to the left lung area set value, the left lung of the patient is abnormal in development; otherwise, the left lung of the patient is normal in development.

23. The method of assessing lung development according to any one of claims 15, 16, 19-21, wherein, Further comprising: In the case that the result of evaluating lung development of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area during the breathing process of the patient respectively is that the right lung is normal in development, the right lung area corresponding to deep inspiration and deep expiration of the patient during the breathing process is obtained, and a right lung area set value corresponding to the age information, the height information and the gender information is determined; lung development of the patient is evaluated based on the right lung area and the right lung area set value.

24. The method of claim 17, wherein the lung development assessment is performed on a subject having a lung disease. Further comprising: ​ In the case that the result of evaluating lung development of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area during the breathing process of the patient respectively is that the right lung is normal in development, the right lung area corresponding to deep inspiration and deep expiration of the patient during the breathing process is obtained, and a right lung area set value corresponding to the age information, the height information and the gender information is determined; lung development of the patient is evaluated based on the right lung area and the right lung area set value.

25. The method of claim 18, wherein the lung development assessment is performed on a subject having a lung disease. Further comprising: ​ In the case that the result of evaluating lung development of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area during the breathing process of the patient respectively is that the right lung is normal in development, the right lung area corresponding to deep inspiration and deep expiration of the patient during the breathing process is obtained, and a right lung area set value corresponding to the age information, the height information and the gender information is determined; lung development of the patient is evaluated based on the right lung area and the right lung area set value.

26. The method of claim 22, wherein the lung development assessment is performed on a subject having a lung disease. 27 Further comprising: In the case that the result of evaluating lung development of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area during the breathing process of the patient respectively is that the right lung is normal in development, the right lung area corresponding to deep inspiration and deep expiration of the patient during the breathing process is obtained, and a right lung area set value corresponding to the age information, the height information and the gender information is determined; lung development of the patient is evaluated based on the right lung area and the right lung area set value.

27. The lung development assessment method according to claim 23, characterized in that, The lung development of the patient is evaluated based on the right lung area and the right lung area set value, including: a second difference value between the right lung area under deep inspiration and the right lung area under deep expiration is calculated; The second difference value and the right lung area set value are used to evaluate lung development of the patient.

28. The method of lung development assessment according to any one of claims 24-26, wherein, The lung development of the patient is evaluated based on the right lung area and the right lung area set value, including: a second difference value between the right lung area under deep inspiration and the right lung area under deep expiration is calculated; The second difference value and the right lung area set value are used to evaluate lung development of the patient.

29. The lung development assessment method according to claim 27, characterized in that, The second difference value and the right lung area set value are used to evaluate lung development of the patient, including: If the second difference is greater than or equal to the right lung area set value, the patient has abnormal right lung development; otherwise, the patient has normal right lung development.

30. The method of claim 28, wherein the lung development assessment is performed on a subject having a lung disease. The second difference and the right lung area set value are used for lung development evaluation of the patient, including: If the second difference is greater than or equal to the right lung area set value, the patient has abnormal right lung development; otherwise, the patient has normal right lung development.

31. A lung field area determination apparatus characterized by comprising: Including: A first acquisition unit is configured to acquire two-dimensional DR left lung images at multiple moments in a breathing process; wherein, before the acquisition of the two-dimensional DR left lung images at multiple moments in the breathing process, a chest detection is performed on a to-be-processed DR image to remove information outside the chest in the to-be-processed DR image, including: calculating a plurality of first gradient amplitudes corresponding to each pixel point in the to-be-processed DR image in a horizontal direction and a plurality of second gradient amplitudes corresponding to each pixel point in a vertical direction, respectively; determining a plurality of total gradient amplitudes based on the plurality of first gradient amplitudes and the plurality of second gradient amplitudes; integrating the plurality of first gradient amplitudes corresponding to the horizontal direction and the plurality of second gradient amplitudes in the vertical direction along a direction perpendicular thereto to obtain a plurality of first integral values and a plurality of second integral values; integrating the plurality of total gradient amplitudes in the vertical direction and the horizontal direction to obtain a plurality of third integral values and a plurality of fourth integral values; a plurality of first local maximum values corresponding to a plurality of first ratios between the plurality of first integral values and corresponding plurality of third integral values; calculating a plurality of first local minimum values and a plurality of second local minimum values corresponding to a plurality of second ratios between the plurality of first ratios and the plurality of second integral values and corresponding plurality of fourth integral values; configuring position information corresponding to a minimum value of the plurality of second local minimum values as a first segmentation position of a neck or a shoulder; determining a maximum value of the plurality of first local maximum values on one side of a center line of the to-be-processed DR image and a minimum value of the plurality of first local minimum values on the other side of the center line; configuring position information corresponding to the maximum value as a thorax side of a second segmentation position; configuring position information corresponding to the minimum value as the other side of the thorax of the second segmentation position; A determination unit is configured to determine a left lung area in the breathing process based on the two-dimensional DR left lung images at multiple moments in the breathing process.

32. The lung field area determination apparatus of claim 31, wherein The determination unit includes a set area acquisition unit configured to acquire a set area corresponding to a single pixel; A statistical unit is configured to respectively count a number of left lung pixels in the two-dimensional DR left lung images at multiple moments in the breathing process; An area calculation unit is configured to calculate a left lung area corresponding to the number of left lung pixels in the two-dimensional DR left lung images at multiple moments in the breathing process based on the set area.

33. The lung field area determination apparatus of claim 32, wherein The area calculation unit includes a left lung area calculation unit; The left lung area calculation unit is configured to multiply the set area by the number of left lung pixels in the two-dimensional DR left lung images at multiple moments in the breathing process to obtain the corresponding left lung area in the breathing process.

34. Apparatus for determining lung field area according to any of claims 31-33, wherein, Further including: A segmentation unit; The segmentation unit is configured to acquire two-dimensional DR lung images at multiple time points in the respiratory process, and perform left lung and right lung segmentation on the two-dimensional DR lung images at multiple time points in the respiratory process to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at multiple time points.

35. The lung field area determination apparatus of claim 34, wherein, The segmentation unit comprises a detection unit. The detection unit is configured to perform rib edge boundary, lung apex boundary, and mediastinum and diaphragm edge detection on left and right chest images of the two-dimensional DR lung images at multiple time points in the respiratory process to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points.

36. The lung field area determination apparatus of claim 34, wherein, The segmentation unit comprises a model and data acquisition unit, a training unit, and an output unit. The model and data acquisition unit is configured to acquire a segmentation model of a preset convolutional neural network and DR lung region label images used for training the segmentation model. The training unit is configured to train the segmentation model by using the DR lung region label images used for training the segmentation model. The output unit is configured to complete left lung and right lung segmentation of the two-dimensional DR lung images at multiple time points in the respiratory process based on the trained segmentation model to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple time points.

37. The lung field area determination apparatus of claim 36, wherein The segmentation unit further comprises a label determination unit. The label determination unit is configured to perform rib edge boundary, lung apex boundary, and mediastinum and diaphragm edge detection on left and right chest images of multiple DR lung region images to obtain DR lung region label images corresponding to the multiple DR lung region images.

38. A lung field area determination apparatus characterized by comprising: It comprises: A first acquisition unit is configured to acquire two-dimensional DR right lung images at multiple time points in the respiratory process. Before the two-dimensional DR right lung images at multiple moments in the respiration process are acquired, chest detection is performed on the to-be-processed DR image, and information outside the chest in the to-be-processed DR image is removed, including: calculating a plurality of first gradient amplitudes corresponding to each pixel point in the to-be-processed DR image in the horizontal direction and a plurality of second gradient amplitudes corresponding to each pixel point in the vertical direction respectively; determining a plurality of total gradient amplitudes based on the plurality of first gradient amplitudes and the plurality of second gradient amplitudes; integrating the plurality of first gradient amplitudes corresponding to the horizontal direction and the plurality of second gradient amplitudes in the vertical direction along the direction perpendicular thereto to obtain a plurality of first integral values and a plurality of second integral values; integrating the plurality of total gradient amplitudes in the vertical direction and the horizontal direction to obtain a plurality of third integral values and a plurality of fourth integral values; a plurality of first local maximum values corresponding to a plurality of first ratios between the plurality of first integral values and the corresponding plurality of third integral values; calculating a plurality of first local minimum values and a plurality of second local minimum values corresponding to a plurality of second ratios between the plurality of first ratios and the plurality of second integral values and the corresponding plurality of fourth integral values; configuring the position information corresponding to the minimum value of the plurality of second local minimum values as the first segmentation position of the neck or the shoulder; determining the maximum value in the plurality of first local maximum values on one side of the center line of the to-be-processed DR image and the minimum value in the plurality of first local minimum values on the other side of the center line; configuring the position information corresponding to the maximum value as the chest on one side of the second segmentation position; and configuring the position information corresponding to the minimum value as the chest on the other side of the second segmentation position. A determination unit is configured to determine the right lung area in the respiration process based on the two-dimensional DR right lung images at multiple moments in the respiration process.

39. The lung field area determination apparatus of claim 38, wherein, The determination unit includes: A set area acquisition unit is configured to acquire a set area corresponding to a single pixel. A statistical unit is configured to respectively count the number of right lung pixels in the two-dimensional DR right lung images at multiple moments in the respiration process. An area calculation unit is configured to respectively calculate the right lung area corresponding to the number of right lung pixels in the two-dimensional DR right lung images at multiple moments in the respiration process based on the set area.

40. The lung field area determination apparatus of claim 39, wherein, The area calculation unit includes a right lung area calculation unit. The right lung area calculation unit is configured to respectively multiply the set area by the number of right lung pixels in the two-dimensional DR right lung images at multiple moments in the respiration process to obtain the corresponding right lung area in the respiration process.

41. Apparatus for determining lung field area according to any of claims 38-40, characterised in that, Further comprising: A segmentation unit; The segmentation unit is configured to acquire the two-dimensional DR lung images at multiple moments in the respiration process before the two-dimensional DR right lung images at multiple moments in the respiration process are acquired, and perform left lung and right lung segmentation on the two-dimensional DR lung images at multiple moments in the respiration process to obtain the two-dimensional DR left lung images and the two-dimensional DR right lung images at multiple moments.

42. The lung field area determination apparatus of claim 41, wherein, The segmentation unit includes a detection unit; The detection unit is configured to detect rib edge boundaries, lung apex boundaries, and mediastinal and diaphragmatic edge boundaries of left and right chest images of the two-dimensional DR lung images at multiple time points in the breathing process, to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at the multiple time points.

43. The lung field area determination apparatus of claim 41, wherein, The segmentation unit comprises a model and data acquisition unit, a training unit, and an output unit. The model and data acquisition unit is configured to acquire a segmentation model of a preset convolutional neural network and DR lung region label images used for training the segmentation model. The training unit is configured to train the segmentation model by using the DR lung region label images used for training the segmentation model. The output unit is configured to complete left and right lung segmentation of the two-dimensional DR lung images at multiple time points in the breathing process based on the trained segmentation model, to obtain two-dimensional DR left lung images and two-dimensional DR right lung images at the multiple time points.

44. The lung field area determination apparatus of claim 43, wherein, The segmentation unit further comprises a label determination unit. The label determination unit is configured to detect rib edge boundaries, lung apex boundaries, and mediastinal and diaphragmatic edge boundaries of left and right chest images of multiple DR lung region images, to obtain DR lung region label images corresponding to the multiple DR lung region images.

45. A lung development assessment device, comprising: The left lung area or the right lung area in the breathing process obtained by the lung field area determination device of any one of claims 31-44 is included or applied; and The second acquisition unit is configured to acquire age information, height information, and gender information of a patient corresponding to the left lung area or the right lung area in the breathing process, and determine a set left lung area and a set right lung area in the breathing process according to the age information, the height information, and the gender information. The first evaluation unit is configured to evaluate lung development of the patient according to the left lung area, the right lung area, the set left lung area, and the set right lung area in the breathing process of the patient, respectively.

46. The lung development assessment device of claim 45, wherein, The first evaluation unit comprises a left lung evaluation unit and a right lung evaluation unit. The left lung evaluation unit is configured to determine that left lung development of the patient is abnormal if the left lung area in the breathing process is less than the set left lung area in the breathing process corresponding thereto, or otherwise, determine that left lung development of the patient is normal; or the left lung evaluation unit is configured to calculate a left lung average area corresponding to the left lung area in the breathing process and calculate a set left lung average area corresponding to the set left lung area in the breathing process, respectively; determine that left lung development of the patient is abnormal if the left lung average area is less than the set left lung average area, or otherwise, determine that left lung development of the patient is normal; and / or The right lung evaluation unit is configured to determine that right lung development of the patient is abnormal if the right lung area in the breathing process is less than the set right lung area in the breathing process corresponding thereto, or otherwise, determine that right lung development of the patient is normal; or the right lung evaluation unit is configured to calculate a right lung average area corresponding to the right lung area in the breathing process and calculate a set right lung average area corresponding to the set right lung area in the breathing process, respectively; determine that right lung development of the patient is abnormal if the right lung average area is less than the set right lung average area, or otherwise, determine that right lung development of the patient is normal.

47. The lung development assessment device of either of claims 45 or 46, wherein, Further comprising: The condition judging unit is configured to judge the condition that the result of the lung development evaluation of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area in the breathing process of the patient is that the right lung is developed normally. The second obtaining unit is configured to obtain the left lung area of the patient in the deep inspiration and the deep expiration in the breathing process, and determine a set left lung area value according to the age information, the height information and the gender information. The second evaluation unit is configured to evaluate the lung development of the patient based on the left lung area in the deep inspiration and the deep expiration and the set left lung area value. Further comprising:

48. The lung development assessment device of either of claims 45 or 46, wherein, The condition judging unit is configured to judge the condition that the result of the lung development evaluation of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area in the breathing process of the patient is that the right lung is developed normally. The second obtaining unit is configured to obtain the right lung area of the patient in the deep inspiration and the deep expiration in the breathing process, and determine a set right lung area value according to the age information, the height information and the gender information. The second evaluation unit is configured to evaluate the lung development of the patient based on the right lung area and the set right lung area value. Further comprising: The condition judging unit is configured to judge the condition that the result of the lung development evaluation of the patient according to the left lung area, the right lung area, the set left lung area and the set right lung area in the breathing process of the patient is that the right lung is developed normally.

49. The lung development assessment device of claim 47, wherein, The second obtaining unit is configured to obtain the right lung area of the patient in the deep inspiration and the deep expiration in the breathing process, and determine a set right lung area value according to the age information, the height information and the gender information. The second evaluation unit is configured to evaluate the lung development of the patient based on the right lung area and the set right lung area value. The second evaluation unit comprises a second left lung evaluation unit. The second left lung evaluation unit is configured to evaluate the lung development of the patient based on the left lung area and the set left lung area value, comprising a first calculation unit and a first comparison unit; wherein the first calculation unit is configured to calculate a first difference value of the left lung area in the deep inspiration and the left lung area in the deep expiration; and wherein the first comparison unit is configured to evaluate the lung development of the patient based on the first difference value and the set left lung area value. The second evaluation unit comprises a second left lung evaluation unit.

50. The lung development assessment device of claim 47, wherein, The second left lung evaluation unit is configured to evaluate the lung development of the patient based on the left lung area and the set left lung area value, comprising a first calculation unit and a first comparison unit; wherein the first calculation unit is configured to calculate a first difference value of the left lung area in the deep inspiration and the left lung area in the deep expiration; and wherein the first comparison unit is configured to evaluate the lung development of the patient based on the first difference value and the set left lung area value. The second evaluation unit comprises a second left lung evaluation unit.

51. The lung development assessment device of claim 48, wherein, The second left lung evaluation unit is configured to evaluate the lung development of the patient based on the left lung area and the set left lung area value, comprising a first calculation unit and a first comparison unit; wherein the first calculation unit is configured to calculate a first difference value of the left lung area in the deep inspiration and the left lung area in the deep expiration; and wherein the first comparison unit is configured to evaluate the lung development of the patient based on the first difference value and the set left lung area value. ​ 52. The lung development assessment device of claim 49, wherein, ​ The second left lung evaluation unit is configured to perform lung development evaluation on the patient based on the left lung area and the left lung area setting value, and includes a first calculation unit and a first comparison unit. The first calculation unit is configured to calculate a first difference between the left lung area in the deep inhalation and the left lung area in the deep exhalation. The first comparison unit is configured to perform lung development evaluation on the patient based on the first difference and the left lung area setting value.

53. The lung development assessment device of any of claims 50-52, wherein, The first comparison unit performs lung development evaluation on the patient based on the first difference and the left lung area setting value, and includes: if the first difference is greater than or equal to the left lung area setting value, the left lung of the patient is abnormal; otherwise, the left lung of the patient is normal.

54. The lung development assessment device of claim 47, wherein, The second evaluation unit includes a second right lung evaluation unit. The second right lung evaluation unit is configured to perform lung development evaluation on the patient based on the right lung area and the right lung area setting value, and includes a second calculation unit and a second comparison unit. The second calculation unit is configured to calculate a second difference between the right lung area in the deep inhalation and the right lung area in the deep exhalation. The second comparison unit is configured to perform lung development evaluation on the patient based on the second difference and the right lung area setting value.

55. The lung development assessment device of claim 48, wherein, The second evaluation unit includes a second right lung evaluation unit. The second right lung evaluation unit is configured to perform lung development evaluation on the patient based on the right lung area and the right lung area setting value, and includes a second calculation unit and a second comparison unit. The second calculation unit is configured to calculate a second difference between the right lung area in the deep inhalation and the right lung area in the deep exhalation. The second comparison unit is configured to perform lung development evaluation on the patient based on the second difference and the right lung area setting value.

56. The lung development assessment device of any of claims 49-52, wherein, The second evaluation unit includes a second right lung evaluation unit. The second right lung evaluation unit is configured to perform lung development evaluation on the patient based on the right lung area and the right lung area setting value, and includes a second calculation unit and a second comparison unit. The second calculation unit is configured to calculate a second difference between the right lung area in the deep inhalation and the right lung area in the deep exhalation. The second comparison unit is configured to perform lung development evaluation on the patient based on the second difference and the right lung area setting value.

57. The lung development assessment device of claim 53, wherein, The second evaluation unit includes a second right lung evaluation unit. The second right lung evaluation unit is configured to perform lung development evaluation on the patient based on the right lung area and the right lung area setting value, and includes a second calculation unit and a second comparison unit. The second calculation unit is configured to calculate a second difference between the right lung area in the deep inhalation and the right lung area in the deep exhalation. The second comparison unit is configured to perform lung development evaluation on the patient based on the second difference and the right lung area setting value.

58. The lung development assessment device of any one of claims 54, 55, 57, wherein, The second comparison unit is configured to perform lung development evaluation on the patient based on the second difference value and a right lung area setting value, and the lung development evaluation includes: if the second difference value is greater than or equal to the right lung area setting value, the patient has abnormal right lung development; otherwise, the patient has normal right lung development.

59. The lung development assessment device of claim 56, wherein, The second comparison unit is configured to perform lung development evaluation on the patient based on the second difference value and a right lung area setting value, and the lung development evaluation includes: if the second difference value is greater than or equal to the right lung area setting value, the patient has abnormal right lung development; otherwise, the patient has normal right lung development.

60. An electronic device, comprising: The method comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the lung field area determination method according to any one of claims 1 to 14.

61. An electronic device, comprising: The method comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the lung development evaluation method according to any one of claims 15 to 30.

62. An electronic device, comprising: The method comprises: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the lung field area determination method according to any one of claims 1 to 14 and the lung development evaluation method according to any one of claims 15 to 30. 63.A computer readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the lung field area determination method according to any one of claims 1 to 14. 64.A computer-readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the lung development evaluation method according to any one of claims 15 to 30. 65.A computer readable storage medium having stored thereon computer program instructions, wherein, The computer program instructions, when executed by the processor, implement the lung field area determination method according to any one of claims 1 to 14 and the lung development evaluation method according to any one of claims 15 to 30.

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