Ultrasonic imaging equipment, method and device for detecting B line, and storage medium

Abstract

The invention discloses ultrasonic imaging equipment, a method and device for detecting a B line and a storage medium. The ultrasonic imaging equipment comprises an ultrasonic probe (01), a transmitting circuit (02), a receiving circuit (03), a beam forming module (04), a processor (05) and a man-machine interaction device (06), wherein the transmitting circuit (02) is used for exciting the ultrasonic probe (01) to transmit ultrasonic beams to the lung; the receiving circuit (03) is used for receiving echoes of the ultrasonic beams to obtain ultrasonic echo signals; and the processor (05) processes the ultrasonic echo signal to obtain at least one frame of lung ultrasonic image, identifies an image sign (at least including a B line) of the lung ultrasonic image, calculates a B line coverage percentage and / or a B line interval of the lung ultrasonic image of which the image sign is the B line, and then sends the B line coverage percentage and / or the B line interval to the man-machine interaction device (06) for display. According to the equipment and the method, quantitative analysis of the B line is realized, and the intelligence degree of lung ultrasonic examination is improved.

Description

Technical Field

[0001] The present invention relates to the field of ultrasonic imaging technology, and particularly to an ultrasonic imaging device, a method, a device and a storage medium for detecting B-lines. Background Art

[0002] Ultrasonic imaging is a medical imaging technology used to image organs and soft tissues in the human body, and plays an important role in clinical medicine. Lung ultrasound has great application value in the differential diagnosis of pulmonary exudative lesions, has good sensitivity and specificity for the diagnosis of various lung diseases, and can even replace chest CT examination and be applied to the diagnosis of lung diseases in emergency critical care medicine. It can save time and cost in clinical practice and save patients' lives in time.

[0003] Different from ultrasonic examinations of other human body parts, lung ultrasonic imaging mostly reflects not the direct image of lung tissue, but a series of artifacts, which can be defined according to the display characteristics of lung ultrasonic images. When examining a normally inflated lung by ultrasound, only the pleura can be detected. However, as the air content decreases, the echo loss effect between the lung and surrounding tissues will decrease accordingly, and ultrasound can reflect the image of deeper regions to a certain extent, generating typical image signs.

[0004] In recent years, in the fields of critical care and emergency, lung ultrasound has been increasingly widely used and emphasized. Identifying typical image signs helps assist medical staff in quickly diagnosing lung diseases. However, current lung ultrasound examinations mainly rely on manual measurement, which is time-consuming and laborious. The examination results are greatly affected by the experience level of operators, and the degree of intelligence is low. Summary of the Invention

[0005] The present invention mainly provides an ultrasonic imaging device, a method, a device and a storage medium for detecting B-lines to improve the degree of intelligence of lung ultrasound examinations.

[0006] According to a first aspect, in one embodiment, an ultrasonic imaging device is provided, including:

[0007] An ultrasonic probe;

[0008] A transmitting circuit for exciting the ultrasonic probe to emit an ultrasonic beam towards the lung;

[0009] A receiving circuit and a beam synthesis module for receiving the echo of the ultrasonic beam to obtain an ultrasonic echo signal;

[0010] A processor for processing the ultrasonic echo signal to obtain at least one frame of lung ultrasonic image; the processor is further configured to identify image signs of the lung ultrasonic image, the image signs at least include B-lines, and calculate parameter information of the lung ultrasonic image with image signs being B-lines, the parameter information including at least one of a B-line coverage percentage and a B-line interval, the B-line coverage percentage being the percentage of the area occupied by B-lines in the lung detection area, and the B-line interval being the distance between adjacent B-lines;

[0011] A human-computer interaction device, connected to the processor, for detecting user input information and displaying detection results, the detection results including the parameter information.

[0012] According to a second aspect, an embodiment provides a method for automatically detecting B-lines in a lung ultrasonic image, including:

[0013] Obtaining at least one frame of lung ultrasonic image;

[0014] Identifying image signs of the lung ultrasonic image, the image signs at least including B-lines;

[0015] Calculating parameter information of the lung ultrasonic image with image signs being B-lines, the parameter information including at least one of a B-line coverage percentage and a B-line interval, the B-line coverage percentage being the percentage of the area occupied by B-lines in the lung detection area, and the B-line interval being the distance between adjacent B-lines;

[0016] Displaying the parameter information.

[0017] According to a third aspect, an embodiment provides a device for automatically detecting B-lines in a lung ultrasonic image, including:

[0018] An acquisition module for acquiring at least one frame of lung ultrasonic image;

[0019] A determination module for determining the lung detection area of the lung ultrasonic image;

[0020] An identification module for identifying image signs of the corresponding lung ultrasonic image within the lung detection area, the image signs at least including B-lines;

[0021] A calculation module for calculating parameter information of the lung ultrasonic image with image signs being B-lines, the parameter information including at least one of a B-line coverage percentage and a B-line interval, the B-line coverage percentage being the percentage of the area occupied by B-lines in the lung detection area, and the B-line interval being the distance between adjacent B-lines;

[0022] A display module for displaying the parameter information.

[0023] According to a fourth aspect, in one embodiment, a computer-readable storage medium is provided, which includes a program that can be executed by a processor to implement the method described above.

[0024] According to the ultrasonic imaging device, method, apparatus, and storage medium for detecting B-lines according to the above embodiments, since the ultrasonic imaging device can automatically detect image signs in a pulmonary ultrasonic image, the image signs at least include B-lines, and can automatically calculate the percentage of the area occupied by B-lines in the pulmonary detection area and / or the distance between B-lines, quantitative analysis of B-lines is achieved, and the degree of intelligence of pulmonary ultrasonic examination is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a schematic structural diagram of an ultrasonic imaging device in an embodiment of the present invention;

[0026] Figure 2 It is a flowchart of a method for automatically detecting B-lines in a pulmonary ultrasonic image in an embodiment of the present invention;

[0027] Figure 3 It is a flowchart of a method for automatically detecting B-lines in a pulmonary ultrasonic image in a specific embodiment of the present invention;

[0028] Figure 4 It is a schematic structural diagram of a processor in a specific embodiment of the present invention;

[0029] Figure 5 It is a schematic diagram of the display effect of displaying a target image and a quantitative result in a specific embodiment of the present invention;

[0030] Figure 6 It is a schematic structural diagram of an apparatus for automatically detecting B-lines in a pulmonary ultrasonic image in an embodiment of the present invention;

[0031] Figure 7 It is a schematic structural diagram of another apparatus for automatically detecting B-lines in a pulmonary ultrasonic image in an embodiment of the present invention;

[0032] Figure 8 It is a schematic structural diagram of yet another apparatus for automatically detecting B-lines in a pulmonary ultrasonic image in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0033] The present invention will be further described in detail below in conjunction with the accompanying drawings through specific embodiments. The features, operations, or characteristics described in the specification can be combined in any suitable manner to form various embodiments. As used in this application, "connection" and "coupling", unless otherwise specified, both include direct and indirect connection (coupling).

[0034] When the air content in the lungs decreases, some exudates, transudates, collagen and blood will increase the density of the lungs, and the echo loss effect between the lungs and surrounding tissues will also be reduced, so that ultrasound can reflect the images of deeper areas to a certain extent. This phenomenon will produce some vertical mixed echoes, called B lines. In lung ultrasound images, B lines appear as discrete vertical reverberation artifacts extending from the pleural line to the bottom of the screen, without loss, and move synchronously with the sliding of the lungs.

[0035] When quantitatively evaluating B-lines, available methods include manually counting the number of B-lines. When the number of B-lines is small, there is no big problem in implementation, but as the number increases, the B-lines will merge with each other and become difficult to distinguish. At this time, it is difficult to use the number of B-lines to quantitatively evaluate the B-lines. Moreover, manual measurement is time-consuming and laborious, and relies on the operator's experience, which is very unfavorable for the promotion and application of lung ultrasound in the field of acute and critical illness. Intelligent automatic identification of B-lines and quantitative analysis tools that support multiple parameters are expected to improve the efficiency and accuracy of B-line quantitative analysis and promote lung ultrasound to play a greater role in the field of acute and critical illness.

[0036] In an embodiment of the present invention, the ultrasonic imaging device identifies image signs of the acquired lung ultrasound image, where the image signs include at least B lines, and then calculates the number of B lines, the percentage of the area occupied by the B lines in the lung detection area, and / or the distance between the B lines, thereby completing automatic detection and quantitative analysis of the B lines.

[0037] Please refer to Figure 1 , Figure 1 A schematic diagram of the structure of an ultrasonic imaging device provided in an embodiment of the present invention, the ultrasonic imaging device includes an ultrasonic probe 01, a transmitting circuit 02, a receiving circuit 03, a beamforming module 04, a processor 05 and a human-computer interaction device 06, and the transmitting circuit 02 and the receiving circuit 03 can be connected to the ultrasonic probe 01 through a transmitting / receiving selection switch 07.

[0038] During the ultrasonic imaging process, the transmitting circuit 02 sends the delayed focused transmitting pulse with a certain amplitude and polarity to the ultrasonic probe 01 through the transmitting / receiving selection switch 07 to stimulate the ultrasonic probe 01 to transmit an ultrasonic beam to the target tissue (for example, organs, tissues, blood vessels, etc. in the human body or animal body), and in the embodiment of the present invention, to transmit an ultrasonic beam to the lungs. After a certain delay, the receiving circuit 03 receives the echo of the ultrasonic beam through the transmitting / receiving selection switch 07 to obtain an ultrasonic echo signal, and sends the echo signal to the beam synthesis module 04. The beam synthesis module 04 performs focusing delay, weighting and channel summing on the ultrasonic echo signal to obtain a beam synthesized ultrasonic echo signal, and then sends the beam synthesized ultrasonic echo signal to the processor 05 for related processing to obtain the desired ultrasonic image or a video file composed of ultrasonic images.

[0039] The ultrasonic probe 01 generally includes an array of multiple array elements. When emitting ultrasonic waves each time, all the array elements of the ultrasonic probe 01 or a part of all the array elements participate in the emission of ultrasonic waves. At this time, each array element or each part of the array elements participating in the emission of ultrasonic waves is respectively excited by an emission pulse and respectively emits ultrasonic waves. The ultrasonic waves respectively emitted by these array elements are superimposed during the propagation process to form a synthetic ultrasonic beam emitted to the scanning target. In the embodiment of the present invention, this synthetic ultrasonic beam is the ultrasonic beam emitted to the lung.

[0040] The human-computer interaction device 06 is connected to the processor 05. For example, the processor 05 can be connected to the human-computer interaction device 06 through an external input / output port. The human-computer interaction device 06 can detect the input information of the user. The input information can be, for example, a control instruction for the ultrasonic wave emission and reception timing, an operation input instruction for editing and annotating the ultrasonic image, or can also include other instruction types. Usually, the operation instructions obtained when the user performs operations such as editing, annotating, and measuring the ultrasonic image are used for the measurement of the target tissue. The human-computer interaction device 06 can include one or a combination of multiple of a keyboard, a mouse, a roller, a trackball, a mobile input device (such as a mobile device with a touch display screen, a mobile phone, etc.), a multi-functional knob, etc. Therefore, the corresponding external input / output port can be a wireless communication module, a wired communication module, or a combination of both. The external input / output port can also be implemented based on USB, a bus protocol such as CAN, and / or a wired network protocol, etc.

[0041] The human-computer interaction device 06 further includes a display, and the display can display the ultrasonic image obtained by the processor 05. In addition, while displaying the ultrasonic image, the display can also provide a graphical interface for human-computer interaction to the user. One or more controlled objects are set on the graphical interface, and the user is provided with the opportunity to input operation instructions using the human-computer interaction device 06 to control these controlled objects, so as to perform corresponding control operations. For example, an icon is displayed on the graphical interface, and the icon can be operated using the human-computer interaction device to perform a specific function, such as the function of annotating the ultrasonic image. In practical applications, the display can be a touch screen display. In addition, the display in this embodiment can include one display or multiple displays.

[0042] In an embodiment of the present invention, the processor 05 is configured to process the ultrasonic echo signals obtained by the beam synthesis module 04 to obtain at least one frame of lung ultrasound image; the processor 05 is further configured to identify the image signs of the lung ultrasound image, the image signs at least include B-lines, and calculate the parameter information of the lung ultrasound image whose image sign is a B-line, the parameter information includes at least one of the number of B-lines, the B-line coverage percentage, and the B-line interval, wherein the B-line coverage percentage is the percentage of the area occupied by the B-lines in the lung detection area, and the B-line interval is the distance between adjacent B-lines. The human-machine interaction device 06 displays the detection result through a display, and the detection result includes the parameter information calculated by the processor 05.

[0043] Based on the ultrasonic imaging device of the above embodiment, the embodiment of the present invention further provides a method for automatically detecting B-lines in a lung ultrasound image. The flowchart thereof can be seen in Figure 2 , and the method may include the following steps:

[0044] Step 101: Obtain at least one frame of lung ultrasound image, for example, obtain a video file including one or more frames of ultrasound images.

[0045] The processor 05 obtains at least one frame of lung ultrasound image collected in real time by the ultrasonic probe 01, or the processor 05 can also read at least one frame of lung ultrasound image from a storage device.

[0046] Step 102: Identify the image signs.

[0047] After the processor 05 obtains at least one frame of lung ultrasound image, it identifies the image signs of each frame of lung ultrasound image, and the image signs at least include B-lines. In one embodiment, the processor 05 can only identify the lung ultrasound images with B-lines from the obtained lung ultrasound images based on a target detection algorithm.

[0048] Step 103: Calculate the parameter information of the B-lines.

[0049] After the processor 05 identifies the B-lines in the lung ultrasound image, it calculates the parameter information of the lung ultrasound image whose image sign is a B-line, and the parameter information includes at least one of the B-line coverage percentage and the B-line interval, wherein the B-line coverage percentage is the percentage of the area occupied by the B-lines in the lung detection area, and the B-line interval is the distance between adjacent B-lines.

[0050] For example, when calculating the B-line coverage percentage of a frame of lung ultrasound image, the lung detection area of the frame of lung ultrasound image can be determined first, and then the percentage of the area occupied by the recognized B-lines in the lung detection area can be calculated; among them, the lung detection area can be determined according to the deep learning method, or the position of the pleural line in the lung ultrasound image can be recognized first, and then the far-field area at the position of the pleural line can be used as the lung detection area of the lung ultrasound image. The area occupied by the B-lines refers to the area of all B-lines in the lung ultrasound image, which can be determined during the recognition process of the B-lines. Specifically, the processor 05 can determine the area pixel points of the recognized B-lines, and then determine the area recognized as the B-lines as the area occupied by the B-lines. For example, the processor 05 can determine the pixel points belonging to the B-lines and use the area defined by each pixel point as the area belonging to the B-lines.

[0051] For example, when calculating the B-line interval in a frame of lung ultrasound image, the position of the pleural line of the frame of lung ultrasound image can be determined first, and then the distance between adjacent B-lines at the position of the pleural line can be calculated according to the recognized B-lines, or the distance between adjacent B-lines at a preset distance from the position of the pleural line can also be calculated.

[0052] Step 104: Display parameter information.

[0053] The processor 05 sends the calculated parameter information to the human-computer interaction device 06 for display.

[0054] The ultrasonic imaging device and the method for automatically detecting B-lines in lung ultrasound images provided by the embodiments of the present invention can automatically detect the image signs of lung ultrasound images and calculate the B-line coverage percentage and / or B-line interval of the lung ultrasound images with the image signs of B-lines, realizing the intelligent recognition of B-lines and the quantitative analysis of multiple parameters, and improving the intelligent level of lung ultrasound examinations. By calculating the B-line coverage percentage, the problem that it is difficult to distinguish due to mutual fusion when the number of B-lines is large and it is impossible to quantitatively evaluate the B-lines by the number of B-lines can be avoided, overcoming the problems of low manual measurement efficiency and the detection results being easily affected by human factors, and improving the measurement efficiency and the accuracy of the detection results.

[0055] To more clearly reflect the purpose of the present invention, further illustrative examples are given on the basis of the above embodiments.

[0056] Please refer to Figure 3 , Figure 3 A specific method for automatically detecting B-lines in lung ultrasound images is provided. In this embodiment, the structure of the processor 05 can be seen in Figure 4 , which may include an acquisition unit 51, an image selection unit 52, an image analysis unit 53, a result selection unit 54, and a scoring unit 55. Specifically, the method may include the following steps:

[0057] Step 201: Obtain at least one frame of lung ultrasound image.

[0058] The processor 05 obtains at least one frame of lung ultrasound image collected in real time by the ultrasound probe 01 through the obtaining unit 51. Alternatively, the obtaining unit 51 can also read at least one frame of lung ultrasound image from the storage device.

[0059] Step 202: Screen out the images to be analyzed.

[0060] After the processor 05 obtains at least one frame of lung ultrasound image, these lung ultrasound images are input into the image selection unit 52. The image selection unit 52 screens out the images to be analyzed from these lung ultrasound images. The images to be analyzed are lung ultrasound images with pathological features. By image screening, useless images such as images without image information, images without lung image signs, and unclear images can be excluded to improve the detection efficiency and reduce false detection.

[0061] Step 203: Identify the image signs of the images to be analyzed.

[0062] After the image selection unit 52 screens out the images to be analyzed, these images to be analyzed are input into the image analysis unit 53. The image analysis unit 53 identifies the image signs of each frame of the images to be analyzed, and at least B-lines are included in the identified image signs.

[0063] Specifically, the image analysis unit 53 first determines the lung detection area of each frame of the images to be analyzed. In one embodiment, the image analysis unit 53 can determine the lung detection area of each frame of the images to be analyzed according to the deep learning method; that is, a large number of lung areas can be pre-calibrated, and then the machine is trained to identify this area through the object detection algorithm. The object detection algorithm can be, for example, the Faster Region Convolutional Neural Network (Faster RCNN) algorithm, etc. In another embodiment, the image analysis unit 53 can also determine the lung detection area of each frame of the images to be analyzed through the image processing method; for example, the position of the pleural line of the images to be analyzed can be first identified, and then the far-field area of the pleural line position is used as the lung detection area of the images to be analyzed. Among them, the position of the pleural line can be determined according to the recognized near-field high-brightness horizontal linear feature, or can also be realized by means of the deep learning method. In addition, sometimes bat signs will appear in the lung ultrasound image, and the bat signs include the pleural line. Therefore, the image analysis unit 53 can also use the bat signs of the images to be analyzed to determine the lung detection area; that is, when the bat signs are identified, the position of the pleural line can be determined from the bat signs.

[0064] After the image analysis unit 53 determines the lung detection area, image signs are identified within the lung detection area. Common image signs in lung ultrasound images include A-lines, B-lines, the bat sign, the seashore sign, the pleural sliding sign, the shred sign, etc. The detected image signs may include B-lines, lung consolidation, etc. In one embodiment, the image analysis unit 53 may identify image signs within the lung detection area according to the deep learning method, that is, a large number of diseases are calibrated, and then the machine is trained to identify through the object detection algorithm, and the object detection algorithm may be, for example, the Faster RCNN algorithm, etc. In another embodiment, the image analysis unit 53 may also use the image processing method to identify image signs within the lung detection area; since the B-line is a discrete vertical reverberation artifact extending from the pleural line to the bottom of the image display screen without loss, according to this characteristic, the vertical linear feature in the direction of the sound beam line can be detected within the lung detection area to obtain the B-line; among them, the identification of the linear feature can be obtained through template matching and other methods. In practical applications, the B-line can be divided into single B-line and diffuse B-line according to the width of the B-line.

[0065] Step 204: Determine the lung ultrasound image with B-lines.

[0066] After the image analysis unit 53 identifies the image signs of each frame of the image to be analyzed, the lung ultrasound image with B-lines is determined from these images to be analyzed according to the image signs.

[0067] Step 205: Calculate the parameter information of the B-line.

[0068] After the image analysis unit 53 determines the lung ultrasound image with B-lines, for each frame of the lung ultrasound image with B-lines, the B-line coverage percentage and / or the B-line interval are calculated according to the identified B-line to achieve the quantitative analysis of the B-line. When calculating the B-line coverage percentage, the image analysis unit 53 may first determine the area occupied by the B-line, and then calculate the percentage of the area occupied by the B-line in its corresponding lung detection area. Among them, the area occupied by the B-line refers to the area occupied by all B-lines in the lung ultrasound image, which can be considered as the area defined by the positions of the B-line pixel points, and the B-line pixel points can be obtained during the process of identifying the B-line. Based on this, when the image analysis unit 53 determines the area occupied by the B-line, it may first determine the area pixel points belonging to the B-line identified within the lung detection area, and determine the area belonging to the B-line as the area occupied by the B-line.

[0069] When the image analysis unit 53 calculates the B-line interval, it may first determine the position of the pleural line of the lung ultrasound image, and then calculate the distance between adjacent B-lines at the position of the pleural line according to the identified B-lines, or alternatively, the distance between adjacent B-lines at a preset distance from the position of the pleural line may also be calculated. By calculating the interval of the B-line, it can assist medical staff in evaluating the lung condition.

[0070] In practical applications, the image analysis unit 53 can also calculate the number of B-lines based on the identified B-lines. During the evaluation of the B-line quantity, when the number of B-lines is small, each B-line can be clearly distinguished. However, as the number of B-lines increases, the B-lines will merge with each other and become difficult to distinguish. At this time, in order to obtain a more accurate quantity of B-lines, the percentage occupied by the B-lines can be calculated. Therefore, in one embodiment, after the image analysis unit 53 identifies the B-lines, it can calculate the number of B-lines when each B-line can be clearly distinguished (the number of B-lines is small), or calculate the B-line coverage percentage, or calculate the number of B-lines and the B-line coverage percentage simultaneously; when each B-line cannot be clearly distinguished (the number of B-lines is large and they merge with each other and are difficult to distinguish), the B-line coverage percentage can be calculated, and the B-line coverage percentage is used to reflect the quantity of B-lines.

[0071] Step 206: Determine the target image.

[0072] After the image analysis unit 53 calculates the parameter information of the B-lines, the result selection unit 54 of the processor 05 can select at least one frame of image from the lung ultrasound images as the target image according to a preset rule. For example, in a specific embodiment, the preset rule can be that the number of B-lines is the largest or the B-line coverage percentage is the largest. The result selection unit 54 can select a frame of image with the largest number of B-lines from the lung ultrasound images as the target image, or select a frame of image with the largest B-line coverage percentage as the target image. In practical applications, the selection criterion for the target image can be set by the user.

[0073] Step 207: Score the target image.

[0074] After the result selection unit 54 determines the target image, the scoring unit 55 scores the selected at least one frame of target image to obtain the scoring result of each frame of target image, and this scoring result reflects the correlation between the target image and the associated disease.

[0075] Among them, the scoring form for scoring the target image can be various forms composed of at least one of numbers, letters, words, etc., and the scoring can be determined according to one or more of the calculated parameter information. For example, one scoring rule can be: when no abnormality is seen in the target image or there are 2 or fewer clear B-lines, the scoring result is determined to be N or 0; when there are more than 3 clear B-lines in the target image, the scoring result is determined to be B1 or 1; when the interval between B-lines in the target image is less than the preset value (diffuse B-lines), the scoring result is determined to be B2 or 2; when the image signs in the target image represent lung consolidation, the scoring result is determined to be C or 3.

[0076] In practical applications, the scoring unit 55 can not only obtain the scoring results at individual positions in the lungs, but also calculate the sum of the scores of the target images corresponding to each scanning position in the lungs, that is, add up the scores of each scanning position to obtain the scoring results of the entire lungs.

[0077] Step 208: Display the target image and the quantitative results.

[0078] After obtaining the scoring results of the target image, the processor 05 can send the selected target image, its corresponding parameter information, and the scoring results to the human-computer interaction device 06 for display. In one embodiment, the human-computer interaction device 06 can also highlight at least one of the lung detection area, the B-line, and the annotation line of the B-line interval in the target image.

[0079] As Figure 5 shown is a schematic diagram of a display effect. Among them, the dotted line indicates the lung detection area. The vertical solid line within this area is the detected B-line. The quantitative analysis results of the target image are shown in the upper right area, and these quantitative analysis results can include the scoring results, the number of B-lines, and the B-line coverage percentage. The table in the lower right area indicates the B-line interval.

[0080] The method for automatically detecting B-lines in lung ultrasound images provided in this embodiment first screens out the images to be analyzed with pathological features from the acquired lung ultrasound images, thereby eliminating useless images, improving the detection efficiency and reducing the false detection rate. Then, it determines the lung detection area of each frame of the image to be analyzed, and identifies the image signs within this lung detection area. The image signs at least include B-lines. Then, it determines the lung ultrasound images with B-lines according to the identified image signs, and then calculates at least one of the B-line coverage percentage, the B-line interval, and the number of B-lines corresponding to these lung ultrasound images respectively, so as to realize the automatic detection and quantitative analysis of B-lines. After that, at least one image can be selected from the lung ultrasound images as the target image according to the calculated parameter information of the B-line, and the target image is scored. Finally, the target image, its corresponding parameter information, and the scoring results are displayed to give a unified and intuitive quantitative analysis result to assist medical staff in jointly judging the patient's lung condition in combination with other examination results. This method overcomes the problem of low efficiency of manual measurement, and at the same time avoids the influence of human factors on the detection results, improves the intelligent level of lung ultrasound examination, and also improves the measurement efficiency and the accuracy of the detection results.

[0081] In the method of the above embodiment, when performing image screening, the processor 05 screens out the to-be-analyzed images with pathological features, then identifies the image signs of the to-be-analyzed images, and then determines the lung ultrasound images with B-lines according to the identified image signs, so as to obtain the lung ultrasound images with B-lines. Different from this, in another specific embodiment, the processor 05 can also screen out only the images with B-lines when performing image screening, that is, directly identify the lung ultrasound images with B-lines from at least one frame of acquired lung ultrasound images. For example, the lung ultrasound images with B-lines can be identified from the acquired lung ultrasound images based on the object detection algorithm.

[0082] The above embodiment is described by taking the determination of the target image and the scoring of the target image as an example. What the human-computer interaction device 06 displays is the target image and its corresponding parameter information and scoring result. In practical applications, the target image may not be scored. After the processor calculates the parameter information and selects at least one frame of image from the lung ultrasound images as the target image according to the preset rules, the human-computer interaction device 06 directly displays the target image and its corresponding parameter information.

[0083] In the method of the above embodiment, the detection of the disease is realized by a fully automatic method. In practical applications, it can also be realized by a method combining automatic and manual operations. For example, automatically identify the B-lines and manually mark the lung ultrasound images without B-lines. Specifically, the image analysis unit 53 of the processor 05 can determine the lung ultrasound images with image signs of non-B-lines from the to-be-analyzed images according to the identified image signs of the to-be-analyzed images; the human-computer interaction device 06 detects the operation instruction of the user to mark the lung ultrasound images with image signs of non-B-lines and sends the operation instruction to the processor 05. At this time, the processor 05 will mark the lung ultrasound images with image signs of non-B-lines according to the operation instruction.

[0084] Such as Figure 6As shown in the figure, a device for automatically detecting B-lines in lung ultrasound images is provided. The device includes an acquisition module 61, a determination module 62, an identification module 63, a calculation module 64, and a display module 65. The acquisition module 61 is configured to acquire at least one frame of lung ultrasound images. The determination module 62 is configured to determine the lung detection region of the lung ultrasound images acquired by the acquisition module 61. For example, the determination module 62 may determine the lung detection region of the lung ultrasound images according to the deep learning method, or the determination module 62 may first identify the position of the pleural line in the lung ultrasound images, and then use the far-field region of the pleural line position as the lung detection region of the frame of lung ultrasound images. The identification module 63 is configured to identify the image signs corresponding to the lung ultrasound images within the lung detection region determined by the determination module 62, and the image signs at least include B-lines. The calculation module 64 is configured to calculate the parameter information of the lung ultrasound images with the image signs being B-lines, and the parameter information includes at least one of the B-line coverage percentage and the B-line interval. The B-line coverage percentage is the percentage of the area occupied by the B-lines in the lung detection region, and the B-line interval is the distance between adjacent B-lines. When the parameter information is the B-line interval, the calculation module 64 is specifically configured to determine the position of the pleural line in the lung ultrasound images with the image signs being B-lines, and then calculate the distance between adjacent B-lines at the pleural line position according to the identified B-lines. When the parameter information is the B-line coverage percentage, the calculation module 64 is configured to calculate the percentage of the area occupied by the B-lines in the corresponding lung detection region according to the identified B-lines to obtain the B-line coverage percentage. Specifically, the calculation module 64 may first determine the region belonging to the B-lines, then determine the region belonging to the B-lines as the area occupied by the B-lines, and then calculate the percentage of the area occupied by the B-lines in the corresponding lung detection region. The display module 65 is configured to display the parameter information calculated by the calculation module 64.

[0085] Based on Figure 6 , such as Figure 7 shown in the figure, another device for automatically detecting B-lines in lung ultrasound images is provided. The device includes an acquisition module 61, a determination module 62, an identification module 63, a calculation module 64, a display module 65, a selection module 66, and a scoring module 67. Among them, the functions of the acquisition module 61, the determination module 62, the identification module 63, and the calculation module 64 are the same as Figure 6The one-to-one correspondence is the same. The selection module 66 is configured to select at least one frame of image from the lung ultrasound images acquired by the acquisition module 61 as the target image according to a preset rule. For example, one frame of image can be selected as the target image. The calculation module 64 can also calculate the number of B-lines. The preset rule can be that the number of B-lines is the largest or the B-line coverage percentage is the largest. At this time, the selection module 66 can select, according to the calculation result of the calculation module 64, one frame of image with the largest number of B-lines from the lung ultrasound images acquired by the acquisition module 61 as the target image, or select one frame of image with the largest B-line coverage percentage as the target image. The display module 65 is configured to display the target image determined by the selection module 66 and its corresponding parameter information. In one embodiment, after the selection module 66 determines the target image, the scoring module 67 can also score the selected at least one frame of target image to obtain a scoring result, which can reflect the correlation between the target image and the associated disease. At this time, the display module 65 can synchronously display the target image, its corresponding parameter information and the scoring result. In addition, the display module 65 can also highlight at least one of the lung detection area, the B-line and the annotation line of the B-line interval in the target image.

[0086] Based on Figure 6 , such as Figure 8 shown, another device for automatically detecting B-lines in lung ultrasound images is provided. The device includes an acquisition module 61, a determination module 62, an identification module 63, a calculation module 64, a display module 65, a screening module 68, a non-B-line determination module 69 and a marking module 60. The acquisition module 61 is configured to acquire at least one frame of lung ultrasound image. The screening module 68 is configured to screen out the images to be analyzed from the lung ultrasound images acquired by the acquisition module 61, and the images to be analyzed are lung ultrasound images with pathological features. Different from the Figure 6 device, in the Figure 8 device, the determination module 62 is configured to determine the lung detection area of the images to be analyzed screened out by the screening module 68, and the identification module 63 is configured to identify the image signs of the images to be analyzed within the lung detection area, and determine the lung ultrasound images with B-lines from the images to be analyzed according to the image signs. The function of the calculation module 64 is the same as that in the Figure 6 device. The non-B-line determination module 69 is configured to determine the lung ultrasound images with image signs of non-B-lines from the images to be analyzed according to the image signs of the images to be analyzed identified by the identification module 63. The marking module 60 is configured to receive an operation instruction for marking the lung ultrasound images with image signs of non-B-lines, and mark the lung ultrasound images with image signs of non-B-lines determined by the non-B-line determination module 69 according to the operation instruction. In addition, the device may also include as Figure 7The selected module 66 and the scoring module 67 in the device shown. At this time, the selected module 66 is used to select at least one frame of image from the images to be analyzed screened by the screening module 68 as the target image according to a preset rule.

[0087] This document is described with reference to various exemplary embodiments. However, those skilled in the art will recognize that changes and modifications can be made to the exemplary embodiments without departing from the scope of this document. For example, various operating steps and the components for performing the operating steps can be implemented in different ways according to a particular application or any number of cost functions associated with the operation of the system (e.g., one or more steps can be deleted, modified, or incorporated into other steps).

[0088] In addition, as will be understood by those skilled in the art, the principles herein can be embodied in a computer program product on a computer-readable storage medium that is preloaded with computer-readable program code. Any tangible, non-transitory computer-readable storage medium can be used, including magnetic storage devices (hard disks, floppy disks, etc.), optical storage devices (CD-ROMs, DVDs, Blu-Ray discs, etc.), flash memory, and / or the like. These computer program instructions can be loaded onto a general-purpose computer, a special-purpose computer, or other programmable data processing devices to form a machine, such that the instructions executed on the computer or other programmable data processing devices can generate a device for implementing the specified functions. These computer program instructions can also be stored in a computer-readable memory, which can direct the computer or other programmable data processing devices to operate in a particular manner, so that the instructions stored in the computer-readable memory can form a manufactured article, including a device for implementing the specified functions. The computer program instructions can also be loaded onto a computer or other programmable data processing devices, thereby performing a series of operating steps on the computer or other programmable devices to generate a computer-implemented process, such that the instructions executed on the computer or other programmable devices can provide steps for implementing the specified functions.

[0089] Although the principles herein have been shown in various embodiments, many modifications of structure, arrangement, proportions, elements, materials, and components, which are particularly adapted to specific environments and operational requirements, can be used without departing from the principles and scope of this disclosure. The above modifications and other changes or amendments will be included within the scope of this document.

[0090] The foregoing detailed description has been presented with reference to various embodiments. However, those skilled in the art will recognize that various modifications and changes can be made without departing from the scope of this disclosure. Accordingly, the contemplation of this disclosure is in an illustrative rather than a restrictive sense, and all such modifications will be included within its scope. Similarly, the advantages of the various embodiments, other advantages, and solutions to problems have been described above. However, benefits, advantages, solutions to problems, and any elements that produce these, or that make them more explicit, should not be construed as critical, required, or essential. As used herein, the term "comprising" and any other variants thereof are inclusive of non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed or inherent to such process, method, system, article, or apparatus. Additionally, as used herein, the term "coupled" and any other variants thereof refer to physical connection, electrical connection, magnetic connection, optical connection, communication connection, functional connection, and / or any other connection.

[0091] Those having skill in the art will recognize that many changes in the details of the above-described embodiments can be made without departing from the basic principles of the invention. Accordingly, the scope of the invention should be determined in accordance with the following claims.

Claims

1. An ultrasonic imaging device, characterized in that, Comprising: An ultrasonic probe; A transmitting circuit for exciting the ultrasonic probe to emit an ultrasonic beam towards the lungs; A receiving circuit and a beam synthesis module for receiving the echo of the ultrasonic beam to obtain an ultrasonic echo signal; A processor for processing the ultrasonic echo signal to obtain at least one frame of lung ultrasonic image; the processor is further configured to identify the image signs of the lung ultrasonic image, the image signs at least include B-lines, and calculate the parameter information of the lung ultrasonic image with image signs being B-lines, the parameter information includes the B-line coverage percentage and the number of B-lines, the B-line coverage percentage is the percentage of the area occupied by B-lines in the lung detection area; wherein, the processor includes an image analysis unit, and after identifying B-lines, the image analysis unit calculates the B-line coverage percentage, or calculates the B-line coverage percentage and the number of B-lines; A human-computer interaction device, which is connected to the processor, for detecting user input information and displaying detection results, and the detection results include the parameter information.

2. The ultrasonic imaging device according to claim 1, wherein, The parameter information further includes the B-line interval, and when calculating the B-line interval, the processor is configured to: Determine the position of the pleural line; Calculate the distance between adjacent B-lines at the pleural line position according to the identified B-lines.

3. The ultrasonic imaging device according to claim 1, characterized in that, The processor is configured to screen out the images to be analyzed from the at least one frame of lung ultrasonic images, the images to be analyzed are lung ultrasonic images with pathological features, identify the image signs of the images to be analyzed, and determine the lung ultrasonic images with B-lines from the images to be analyzed according to the image signs.

4. The ultrasonic imaging device according to claim 3, wherein, The processor further determines the lung ultrasonic images with image signs being non-B-lines from the images to be analyzed according to the identified image signs of the images to be analyzed; the human-computer interaction device is further configured to detect an operation instruction for marking the lung ultrasonic images with image signs being non-B-lines by the user, and send the operation instruction to the processor; The processor is further configured to mark the lung ultrasonic images with image signs being non-B-lines according to the operation instruction.

5. The ultrasonic imaging device according to claim 1, wherein The processor is configured to identify the lung ultrasonic images with B-lines from the at least one frame of lung ultrasonic images.

6. The ultrasonic imaging device according to any one of claims 1 to 5, characterized in that, When identifying the image signs of the lung ultrasonic images, the processor is configured to: Determine the lung detection area of the lung ultrasonic image; Identify the image signs within the lung detection area.

7. The ultrasonic imaging device according to claim 6, wherein When determining the lung detection area of the lung ultrasonic image, the processor is configured to: identify the position of the pleural line of the lung ultrasonic image, and use the far-field area of the pleural line position as the lung detection area of the lung ultrasonic image.

8. The ultrasonic imaging device according to claim 6, characterized in that, The parameter information includes the B-line coverage percentage, and when calculating the B-line coverage percentage, the processor is configured to: calculate the percentage of the area occupied by the identified B-lines in the corresponding lung detection area.

9. The ultrasonic imaging device according to claim 8, characterized in that, The processor is configured to determine the area identified as belonging to B-lines as the area occupied by B-lines.

10. The lung ultrasound imaging device according to claim 6, characterized in that, The image sign is a B-line, and the processor is configured to detect the vertical linear features in the direction of the sound beam line within the lung detection area to obtain the corresponding image sign.

11. The ultrasonic imaging device according to claim 1, characterized in that, After calculating the parameter information, the processor is further configured to select at least one frame of image from the lung ultrasonic images as the target image according to a preset rule; The human-computer interaction device is used to display the target image and its corresponding parameter information.

12. The ultrasonic imaging device according to claim 1, wherein After calculating the parameter information, the processor is further configured to select at least one frame of image from the lung ultrasound images as the target image according to a preset rule, and score the selected at least one frame of target image to obtain a scoring result, where the scoring result reflects the correlation between the target image and the associated disease.

13. The ultrasonic imaging device according to claim 11 or 12, characterized in that, The parameter information further includes the number of B-lines. The target image is one frame of image, and the preset rule includes: the largest number of B-lines or the largest B-line coverage percentage.

14. The ultrasonic imaging device according to claim 12, characterized in that, The processor is further configured to calculate the sum of the scores of the target images corresponding to each scanning position of the lungs to obtain the scoring result of the entire lungs.

15. The ultrasonic imaging device according to claim 12, wherein, The human-computer interaction device is used to display the target image, its corresponding parameter information, and the scoring result.

16. The ultrasonic imaging device according to claim 11 or 12, characterized in that, The human-computer interaction device is further configured to highlight at least one of the lung detection area, the B-lines, and the annotation lines of the B-line intervals in the target image.

17. The ultrasonic imaging device according to claim 1, characterized in that, The processor is further configured to read at least one frame of lung ultrasound image from the storage device.

18. A method for automatically detecting B-lines in lung ultrasound images, characterized in that, Comprising: Obtain at least one frame of lung ultrasound image; Identify the image signs of the lung ultrasound image, where the image signs at least include B-lines; Calculate the parameter information of the lung ultrasound image with the image sign of B-lines, where the parameter information includes the B-line coverage percentage and the number of B-lines, and the B-line coverage percentage is the percentage of the area occupied by the B-lines in the lung detection area; wherein, after identifying the B-lines, calculate the B-line coverage percentage, or calculate the B-line coverage percentage and the number of B-lines; Display the parameter information.

19. The method according to claim 18, wherein The parameter information further includes the B-line interval, and the calculation of the parameter information of the lung ultrasound image with the image sign of B-lines includes: Determine the position of the pleural line of the lung ultrasound image with the image sign of B-lines; Calculate the distance between adjacent B-lines at the pleural line position according to the identified B-lines.

20. The method according to claim 18, characterized in that, The identification of the image signs of the lung ultrasound image includes: Screen out the images to be analyzed from the lung ultrasound images, where the images to be analyzed are lung ultrasound images with pathological features; Identify the image signs of the images to be analyzed, and determine the lung ultrasound images with B-lines from the images to be analyzed according to the image signs.

21. The method according to claim 20, wherein Further comprising: Determine the lung ultrasound images with image signs other than B-lines from the images to be analyzed according to the identified image signs of the images to be analyzed; Receive an operation instruction for marking the lung ultrasound images with image signs other than B-lines, and mark the lung ultrasound images with image signs other than B-lines according to the operation instruction.

22. The method according to any one of claims 18 to 21, characterized in that The identification of the image signs of the lung ultrasound image includes: Determine the lung detection area of the lung ultrasound image; Identify the image signs within the lung detection area.

23. The method according to claim 22, wherein The determination of the lung detection area of the lung ultrasound image includes: Determine the lung detection area of the lung ultrasound image according to the deep learning method; Or, Identify the position of the pleural line of the lung ultrasound image, and use the far-field area of the pleural line position as the lung detection area of this frame of lung ultrasound image.

24. The method according to claim 22, wherein The parameter information includes the B-line coverage percentage, and the calculation of the parameter information of the lung ultrasound image with the image sign of B-lines includes: Calculate the percentage of the area occupied by the identified B-line in its corresponding lung detection area.

25. The method according to claim 24, wherein The step of calculating the percentage of the area occupied by the identified B-line in its corresponding lung detection area includes: Determine the area identified as belonging to the B-line and define it as the area occupied by the B-line; Calculate the percentage of the area occupied by the B-line in its corresponding lung detection area.

26. The method according to claim 18, wherein After calculating the parameter information of the lung ultrasound image with the image sign of B-line, the method further includes: Select at least one frame of image from the lung ultrasound images as the target image according to a preset rule; Display the target image and its corresponding parameter information.

27. The method according to claim 18, wherein After calculating the parameter information of the lung ultrasound image with the image sign of B-line, the method further includes: Score the selected at least one frame of target image to obtain a scoring result. The target image is selected from the lung ultrasound images according to a preset rule, and the scoring result reflects the correlation between the target image and the associated disease.

28. The method according to claim 26 or 27, wherein The parameter information further includes the number of B-lines. The target image is one frame of image, and the preset rule includes: the largest number of B-lines or the largest B-line coverage percentage.

29. The method according to claim 27, wherein It further includes: Calculate the sum of the scores of the target images corresponding to each scanning position of the lung to obtain the scoring result of the entire lung.

30. The method according to claim 27, wherein When displaying the parameter information, the method further includes: synchronously displaying the target image and its corresponding scoring result.

31. The method according to claim 26 or 27, characterized in that, It further includes: Highlight at least one of the lung detection area, B-line, and the marking line of the B-line interval in the target image.

32. An ultrasonic imaging device, characterized in that, It includes: An ultrasonic probe; A transmitting circuit for exciting the ultrasonic probe to emit an ultrasonic beam towards the lung; A receiving circuit and a beam synthesis module for receiving the echo of the ultrasonic beam to obtain an ultrasonic echo signal; A processor for processing the ultrasonic echo signal to obtain a video file including multiple frames of lung ultrasound images; the processor is further used to determine the lung detection area of the lung ultrasound image and identify the image signs of the lung ultrasound image within the lung detection area, and the image signs at least include B-lines; the processor is further used to determine the quantitative parameter information of the lung ultrasound image with the image sign of B-line, and the quantitative parameter information includes the B-line coverage percentage and the number of B-lines; wherein, the processor includes an image analysis unit, and after the image analysis unit identifies the B-line, it calculates the B-line coverage percentage, or calculates the B-line coverage percentage and the number of B-lines; A human-computer interaction device, which is connected to the processor and is used to detect the user's input information and display the detection result, and the detection result includes the parameter information.

33. The ultrasonic imaging device according to claim 32, wherein, The processor is used to screen out the images to be analyzed from the multiple frames of lung ultrasound images in the video file. The images to be analyzed are lung ultrasound images with pathological features, identify the image signs of the images to be analyzed, and determine the lung ultrasound images with B-lines from the images to be analyzed according to the image signs.

34. The ultrasonic imaging device according to claim 32, wherein, When the processor determines the lung detection area of the lung ultrasound image, it is used to: identify the position of the pleural line of the lung ultrasound image and use the far-field area of the pleural line position as the lung detection area of the lung ultrasound image.

35. The ultrasonic imaging device according to claim 32, wherein, The parameter information includes the B-line coverage percentage, and when determining the B-line coverage percentage, the processor is configured to: calculate the percentage of the area occupied by the recognized B-line in its corresponding lung detection area based on the recognized B-line.

36. The ultrasonic imaging device according to claim 35, wherein, The processor is configured to determine the area recognized as belonging to the B-line and determine it as the area occupied by the B-line.

37. The ultrasonic imaging device according to claim 32, wherein The parameter information includes the B-line interval, and when determining the B-line interval, the processor is configured to: Determine the position of the pleural line; Calculate the distance between adjacent B-lines at the position of the pleural line based on the recognized B-lines, or calculate the distance between adjacent B-lines at a preset position from the pleural line based on the recognized B-lines.

38. The ultrasonic imaging device according to claim 32, characterized in that, The parameter information includes the number of B-lines, and when determining the number of B-lines, the processor is configured to: count the number of B-lines recognized in the lung detection area of the lung ultrasound image.

39. A computer-readable storage medium, characterized in that It includes a program that can be executed by a processor to implement the method according to any one of claims 18 to 31.

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