Schistosomiasis suspicious focus area calibration system based on deep learning

Abstract

The invention discloses a schistosomiasis suspicious focus area calibration system based on deep learning, relates to the technical field of image fusion, and improves the positioning accuracy of a focus area in B ultrasonic image data. According to the method, three-channel feature extraction operation is carried out on B-mode ultrasound image data of each focus, then focus suspicious areas, grade fibrosis curves and displacement change vectors are sequentially extracted from the B-mode ultrasound image data of each focus, and a multi-layer image detection network is set; inputting the focus B ultrasonic image data at different shooting angles into a multi-layer image detection network, and performing fusion detection on each focus B ultrasonic image data according to the focus suspicious area, the grade fibrosis curve and the displacement change vector of each focus B ultrasonic image data, thereby obtaining a multi-view dynamic focus fiber image.

Description

Technical Field

[0001] The present invention relates to the technical field of image fusion, and in particular to a schistosomiasis suspected lesion area calibration system based on deep learning. Background Art

[0002] Schistosomiasis is a parasitic disease that seriously endangers human health. Timely and accurate detection and diagnosis are crucial for the prevention and treatment of schistosomiasis. Currently, B-ultrasound examination is one of the commonly used methods for diagnosing schistosomiasis, which can intuitively present the lesions in the patient's body. However, the existing B-ultrasound diagnosis method for schistosomiasis has certain limitations.

[0003] On the one hand, traditional B-ultrasound image analysis can only obtain images from a single or a few fixed angles, which makes it difficult to fully reflect the true characteristics and dynamic changes of the lesions. Since the lesions may have different shapes, positions and characteristics at different angles, images from a single angle may miss some important information, leading to misdiagnosis or missed diagnosis.

[0004] On the other hand, the identification and analysis of lesion areas in B-ultrasound images mostly rely on the subjective judgment and experience of doctors, lacking objective and accurate quantitative standards and automated processing methods. This not only increases the workload of doctors, but also the diagnostic results between different doctors may vary to a certain extent, affecting the accuracy and consistency of the diagnosis. For this reason, a schistosomiasis suspected lesion area calibration system based on deep learning is provided. Summary of the invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide a schistosomiasis suspected lesion area calibration system based on deep learning.

[0006] In order to achieve the above object, the present invention provides the following technical solutions: The schistosomiasis suspected lesion area calibration system based on deep learning includes a B-ultrasound image acquisition module, a lesion area positioning module, and an image fusion detection module; The detection B-ultrasound image acquisition module is used to acquire a plurality of B-ultrasound image data of lesions of the patient from multiple shooting angles; The lesion area positioning module is used to perform a three-channel feature extraction operation on each lesion B-ultrasound image data, and then extract the lesion suspicious area, grade fibrosis curve and displacement change vector from each lesion B-ultrasound image data in sequence; The image fusion detection module is used to set up a multi-layer image detection network, input the lesion B-ultrasound image data at different shooting angles into the multi-layer image detection network, and perform fusion detection on each lesion B-ultrasound image data according to the lesion suspicious area, grade fibrosis curve and displacement change vector of each lesion B-ultrasound image data, so as to obtain a multi-view dynamic lesion fiber image.

[0007] Furthermore, the acquisition process of the lesion B-ultrasound image data includes: The detection B-ultrasound image acquisition module is equipped with six multi-parameter B-ultrasound devices controlled by free mechanical arms, each of which has a built-in convex array probe and a high-frequency linear array probe; Before B-ultrasound image acquisition, the convex array probe in each multi-parameter B-ultrasound device first scans the patient's entire liver area and simultaneously generates a corresponding three-dimensional volume image of the liver; The image features of the schistosomiasis lesion area are obtained through the Internet, and then the suspicious lesion area is traversed in the three-dimensional volume image of the liver according to the image features. Then, based on the distribution of the suspicious lesion area in the three-dimensional volume image of the liver in the patient's liver area at various shooting angles, various multi-parameter B-ultrasound devices use high-frequency linear array probes to perform multi-angle composite scanning on the patient; Then, when the long axis direction of each high-frequency linear array probe is deflected to the left or right by no more than 15°, a plurality of B-ultrasound image data of the lesion are taken in chronological order at various shooting angles.

[0008] Furthermore, the shooting angles of the multi-angle composite scan include the subxiphoid sagittal section, the left subcostal transverse section, the right subcostal section, the right intercostal oblique section, and the high-frequency linear array probe parallel and oblique to the long axis of the liver.

[0009] Furthermore, the three-channel feature extraction operation includes structure channel feature extraction, texture channel feature extraction and dynamic channel feature extraction.

[0010] Furthermore, the process of extracting the structural channel features includes: The B-ultrasound image data of each lesion is grayed, and then the corresponding curvature feature matrix is ​​established according to each grayed pixel in the grayed B-ultrasound image data of the lesion. The curvature feature matrix is ​​expressed as ,in , , , Grayscale pixels represent the second-order partial derivatives and first-order partial derivatives in the x direction, and the second-order partial derivatives and first-order partial derivatives in the y direction respectively; Then, the eigenvalue decomposition is performed according to the curvature feature matrix of each grayscale pixel to obtain the eigenvalue α 1 and α 2, and then through the characteristic value α of each grayscale pixel 1 and α 2 Get its corresponding characteristic response value; When there is a fibrous structure in the B-ultrasound image data of the lesion, the second-order derivative along the x-axis direction is small, while the second-order derivative along the y-axis direction is large, so that the characteristic response value of the grayscale pixel located in the fibrous structure is significantly higher than the characteristic response value of the grayscale pixel in the non-fibrous structure; Then, a characteristic response value threshold is set, and grayscale pixels whose characteristic response values ​​are less than or equal to the characteristic response value threshold are eliminated, and grayscale pixels whose characteristic response values ​​are less than or equal to the characteristic response value threshold are retained, thereby segmenting suspicious lesion areas in the B-ultrasound image data of each lesion.

[0011] Furthermore, the process of extracting the texture channel features includes: Grayscale the suspicious lesion areas in the B-ultrasound image data of each lesion, and preset the texture detection path and texture inspection angle; Texture is formed by the repetition of grayscale in spatial position, so when the grayscale pixels in the grayscale image correspond to the same object, there is a specific grayscale relationship between the two grayscale pixels even if they are separated by a certain distance and direction; Generate a grayscale co-occurrence matrix corresponding to the suspicious lesion area from the texture inspection angle according to the texture detection path. It should be noted that each element in the grayscale co-occurrence matrix represents the probability of any grayscale element appearing simultaneously with other grayscale elements in the suspicious lesion area under the texture detection path and a specific texture inspection angle; According to the grayscale matrix of each grayscale element, the energy value of each grayscale element is obtained, and the energy value of each grayscale element is mapped to the suspicious lesion area in the form of point cloud distribution; When the patient's liver tissue is normal, the energy value distribution in the suspicious lesion area has strong regularity and uniformity. However, when the patient's liver tissue has accumulation of schistosoma fibrils, the energy distribution of each grayscale element in the suspicious lesion area is disordered, and the energy value is smaller than that in normal conditions. Then, an energy threshold is set according to the energy value of the liver tissue under normal conditions, and then for the grayscale elements in the suspicious lesion area whose energy value is less than the energy threshold, the corresponding grayscale elements are recorded as suspicious lesion elements, otherwise the corresponding grayscale elements are ignored; Setting multiple energy value level intervals, and setting the same label for suspicious lesion elements in the same energy value level interval, connecting adjacent suspicious lesion elements with the same label in the suspicious lesion area in sequence, and then dividing a number of texture boundary lines in the suspicious lesion area; The grayscale pixels between two texture boundary lines on adjacent plane positions are marked, and then several graded fibrosis curves are drawn in the suspicious lesion area. It should be noted that the grade of each graded fibrosis curve is equal to the average value of the grade interval of the energy value associated with the corresponding two texture boundary lines.

[0012] Furthermore, the process of extracting dynamic channel features includes: The suspicious lesion areas corresponding to several lesion B-ultrasound image data taken at the same shooting angle are arranged in chronological order, the maximum width of each grade fibrosis curve in the suspicious lesion area is obtained, and the time series displacement value between the same grayscale pixel in the grade fibrosis curve in the adjacent time sequence is obtained, and the maximum value of the time series displacement value is counted; Then, a dynamic tracking window is established with the maximum width set as the length and the maximum value of the time series displacement value as the width, and then the displacement change position of the grayscale pixel in each level fiberization curve in the time sequence is captured in sequence through the dynamic tracking window, and the corresponding displacement change vector is generated.

[0013] Furthermore, the process of fusing the data image of the lesion B-ultrasound image data includes: The image fusion detection module is provided with a multi-layer image detection network for performing data image fusion on the lesion B-ultrasound image data of the same shooting angle; According to the shooting angle and time sequence of each lesion B-ultrasound image data, the suspicious lesion area in each lesion B-ultrasound image data is input into the multi-layer image detection network from six directions and time sequence; A number of dynamic detection neurons are set according to the number of grayscale pixels in the suspected lesion area, and the grayscale elements in the suspected lesion area are input into each dynamic detection neuron according to each time sequence; A number of image detection time points are set according to the number of B-ultrasound image data of lesions captured from various shooting angles by the detection B-ultrasound image acquisition module, and then each time an image detection time point ends, the dynamic detection neuron moves to a corresponding position at the next image detection time point according to the displacement change vector contained in the grayscale element contained therein; At the same time, each shooting angle is used as the main shooting angle, and the other shooting angles are used as secondary shooting angles, so that the dynamic detection neurons of the main shooting angle do not perform any operation, and the other dynamic detection neurons of the secondary shooting angles are transformed according to the angle between the secondary shooting angle and the dynamic detection neurons of the main shooting angle, so as to generate dynamic lesion fiber images under different shooting angles; Furthermore, the process of detecting the fusion result of the lesion B-ultrasound image data includes: The dynamic lesion fiber images at various shooting angles are mapped to the multi-layer image detection network according to their corresponding main shooting angles, since each dynamic lesion fiber image corresponds to the same patient lesion area; A pixel difference threshold is set, and the pixel values ​​of the grayscale pixels corresponding to the same position and the same time sequence are compared with each other. If the pixel difference between the pixel values ​​of each grayscale pixel is below the pixel difference threshold, the pixel values ​​of the grayscale pixels corresponding to the same position and the same time sequence are directly accumulated and averaged, and then reassigned to each grayscale pixel; Otherwise, the grayscale pixels whose pixel difference between two grayscale pixels is greater than or equal to the pixel difference threshold are eliminated, and then the pixel values ​​of the retained grayscale pixels are accumulated and averaged, and then the grayscale pixels are reassigned; Repeat the above pixel value averaging operation to obtain a multi-view dynamic lesion fiber image.

[0014] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention extracts suspicious lesion areas, graded fibrosis curves and displacement change vectors from the B-ultrasound image data of each lesion in sequence through a three-channel feature extraction operation, thereby improving the efficiency of feature extraction and reducing interference from human factors. At the same time, through the analysis of the graded fibrosis curves and displacement change vectors, the development status and dynamic changes of the lesions can be deeply understood.

[0015] 2. By fusing and detecting the lesion B-ultrasound image data from different shooting angles, the detection capability of the lesion is enhanced, and a clearer and more accurate multi-angle dynamic lesion fiber image is obtained. This helps to accurately determine the nature and development stage of the lesion and improve the diagnostic accuracy and treatment effect of schistosomiasis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0018] like Figure 1 As shown, the schistosomiasis suspected lesion area calibration system based on deep learning includes a B-ultrasound image acquisition module, a lesion area positioning module and an image fusion detection module; The detection B-ultrasound image acquisition module is used to acquire a plurality of B-ultrasound image data of lesions of the patient from multiple shooting angles; The lesion area positioning module is used to perform a three-channel feature extraction operation on each lesion B-ultrasound image data, and then extract the lesion suspicious area, grade fibrosis curve and displacement change vector from each lesion B-ultrasound image data in sequence; The image fusion detection module is used to set up a multi-layer image detection network, input the lesion B-ultrasound image data at different shooting angles into the multi-layer image detection network, and perform fusion detection on each lesion B-ultrasound image data according to the lesion suspicious area, grade fibrosis curve and displacement change vector of each lesion B-ultrasound image data, so as to obtain a multi-view dynamic lesion fiber image.

[0019] Further, the working principle of the present invention is described below by way of examples: The detection B-ultrasound image acquisition module is equipped with six multi-parameter B-ultrasound devices controlled by free mechanical arms, each of which has a built-in convex array probe and a high-frequency linear array probe; Before B-ultrasound image acquisition, the convex array probe in each multi-parameter B-ultrasound device first scans the patient's entire liver area and simultaneously generates a corresponding three-dimensional volume image of the liver; The image features of the schistosomiasis lesion area are obtained through the Internet, and then the suspicious lesion area is traversed in the three-dimensional volume image of the liver according to the image features. Then, based on the distribution of the suspicious lesion area in the three-dimensional volume image of the liver in the patient's liver area at various shooting angles, various multi-parameter B-ultrasound devices use high-frequency linear array probes to perform multi-angle composite scanning on the patient; The shooting angles of the multi-angle composite scan include the sagittal section below the xiphoid process, the transverse section below the left rib, the section below the right costal margin, the oblique section between the right ribs, and the high-frequency linear array probe parallel to and oblique to the long axis of the liver. When the long axis direction of each high-frequency linear array probe is deflected to the left or right by no more than 15°, a number of B-ultrasound image data of the lesion are taken in chronological order at each shooting angle.

[0020] Further, the detection B-ultrasound image acquisition module sends all lesion B-ultrasound image data to the lesion area positioning module; The lesion area positioning module performs a three-channel feature extraction operation on any lesion B-ultrasound image data, wherein the three-channel feature extraction operation includes structural channel feature extraction, texture channel feature extraction, and dynamic channel feature extraction; The process of structural channel feature extraction includes: The B-ultrasound image data of each lesion is grayed, and then the corresponding curvature feature matrix is ​​established according to each grayed pixel in the grayed B-ultrasound image data of the lesion. The curvature feature matrix is ​​expressed as ,in , , , Grayscale pixels represent the second-order partial derivatives and first-order partial derivatives in the x direction, and the second-order partial derivatives and first-order partial derivatives in the y direction respectively; Then, the eigenvalue decomposition is performed according to the curvature feature matrix of each grayscale pixel to obtain the eigenvalue α 1 and α 2 , and then through the characteristic value α of each grayscale pixel 1 and α 2 Get its corresponding characteristic response value; When there is a fibrous structure in the B-ultrasound image data of the lesion, the second-order derivative along the x-axis direction is small, while the second-order derivative along the y-axis direction is large, so that the characteristic response value of the grayscale pixel located in the fibrous structure is significantly higher than the characteristic response value of the grayscale pixel in the non-fibrous structure; Then, a characteristic response value threshold is set, and grayscale pixels whose characteristic response values ​​are less than or equal to the characteristic response value threshold are eliminated, and grayscale pixels whose characteristic response values ​​are less than or equal to the characteristic response value threshold are retained, thereby segmenting suspicious lesion areas in the B-ultrasound image data of each lesion.

[0021] The process of texture channel feature extraction includes: Grayscale processing is performed on the suspicious lesion area in the B-ultrasound image data of each lesion, and a texture detection path and a texture inspection angle are preset, and the texture inspection angles include 0°, 45°, 90° and 135°; Texture is formed by the repetition of grayscale in spatial position, so when the grayscale pixels in the grayscale image correspond to the same object, there is a specific grayscale relationship between the two grayscale pixels even if they are separated by a certain distance and direction; Generate a grayscale co-occurrence matrix corresponding to the suspicious lesion area from the texture inspection angle according to the texture detection path. It should be noted that each element in the grayscale co-occurrence matrix represents the probability of any grayscale element appearing simultaneously with other grayscale elements in the suspicious lesion area under the texture detection path and a specific texture inspection angle; According to the grayscale matrix of each grayscale element, the energy value of each grayscale element is obtained, and the energy value of each grayscale element is mapped to the suspicious lesion area in the form of point cloud distribution; When the patient's liver tissue is normal, the energy value distribution in the suspicious lesion area has strong regularity and uniformity. However, when the patient's liver tissue has accumulation of schistosoma fibrils, the energy distribution of each grayscale element in the suspicious lesion area is disordered, and the energy value is smaller than that in normal conditions. Then, an energy threshold is set according to the energy value of the liver tissue under normal conditions, and then for the grayscale elements in the suspicious lesion area whose energy value is less than the energy threshold, the corresponding grayscale elements are recorded as suspicious lesion elements, otherwise the corresponding grayscale elements are ignored; Setting multiple energy value level intervals, and setting the same label for suspicious lesion elements in the same energy value level interval, connecting adjacent suspicious lesion elements with the same label in the suspicious lesion area in sequence, and then dividing a number of texture boundary lines in the suspicious lesion area; The grayscale pixels between two texture boundary lines on adjacent plane positions are marked, and then several graded fibrosis curves are drawn in the suspicious lesion area. It should be noted that the grade of each graded fibrosis curve is equal to the average value of the grade interval of the energy value associated with the corresponding two texture boundary lines.

[0022] The process of dynamic channel feature extraction includes: The suspicious lesion areas corresponding to several lesion B-ultrasound image data taken at the same shooting angle are arranged in chronological order, the maximum width of each grade fibrosis curve in the suspicious lesion area is obtained, and the time series displacement value between the same grayscale pixel in the grade fibrosis curve in the adjacent time sequence is obtained, and the maximum value of the time series displacement value is counted; Then, a dynamic tracking window is established with the maximum width set as the length and the maximum value of the time series displacement value set as the width, and then the displacement change position of the grayscale pixel in each level fibrosis curve in the time sequence is captured in sequence through the dynamic tracking window, and a corresponding displacement change vector is generated; When the three-channel feature extraction operation is completed for all lesion B-ultrasound image data, the lesion area positioning module sends all three-channel feature extraction operation results to the dual-stage inspection module.

[0023] Furthermore, the image fusion detection module is provided with a multi-layer image detection network for performing data image fusion on the lesion B-ultrasound image data of the same shooting angle, and the specific process includes: According to the shooting angle and time sequence of each lesion B-ultrasound image data, the suspicious lesion area in each lesion B-ultrasound image data is input into the multi-layer image detection network from six directions and time sequence; A number of dynamic detection neurons are set according to the number of grayscale pixels in the suspected lesion area, and the grayscale elements in the suspected lesion area are input into each dynamic detection neuron according to each time sequence; A number of image detection time points are set according to the number of B-ultrasound image data of lesions captured from various shooting angles by the detection B-ultrasound image acquisition module, and then each time an image detection time point ends, the dynamic detection neuron moves to a corresponding position at the next image detection time point according to the displacement change vector contained in the grayscale element contained therein; At the same time, each shooting angle is used as the main shooting angle, and the other shooting angles are used as secondary shooting angles, so that the dynamic detection neurons of the main shooting angle do not perform any operation, and the other dynamic detection neurons of the secondary shooting angles are transformed according to the angle between the secondary shooting angle and the dynamic detection neurons of the main shooting angle, so as to generate dynamic lesion fiber images under different shooting angles; The dynamic lesion fiber images at various shooting angles are mapped to the multi-layer image detection network according to their corresponding main shooting angles, since each dynamic lesion fiber image corresponds to the same patient lesion area; A pixel difference threshold is set, and the pixel values ​​of the grayscale pixels corresponding to the same position and the same time sequence are compared with each other. If the pixel difference between the pixel values ​​of each grayscale pixel is below the pixel difference threshold, the pixel values ​​of the grayscale pixels corresponding to the same position and the same time sequence are directly accumulated and averaged, and then reassigned to each grayscale pixel; Otherwise, the grayscale pixels whose pixel difference between two grayscale pixels is greater than or equal to the pixel difference threshold are eliminated, and then the pixel values ​​of the retained grayscale pixels are accumulated and averaged, and then the grayscale pixels are reassigned; Repeat the above pixel value averaging operation to obtain a multi-view dynamic lesion fiber image.

[0024] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A deep learning-based schistosomiasis suspected lesion area calibration system, characterized by: It includes a B-ultrasound image acquisition module, a lesion area positioning module and an image fusion detection module; The detection B-ultrasound image acquisition module is used to acquire a plurality of B-ultrasound image data of lesions of the patient from multiple shooting angles; The lesion area positioning module is used to perform a three-channel feature extraction operation on each lesion B-ultrasound image data, and then extract the lesion suspicious area, grade fibrosis curve and displacement change vector from each lesion B-ultrasound image data in sequence; The three-channel feature extraction operation includes structural channel feature extraction, texture channel feature extraction and dynamic channel feature extraction; the image fusion detection module is used to set up a multi-layer image detection network, input the lesion B-ultrasound image data under different shooting angles into the multi-layer image detection network, and perform fusion detection on each lesion B-ultrasound image data according to the lesion suspicious area, grade fibrosis curve and displacement change vector of each lesion B-ultrasound image data, so as to obtain a multi-view dynamic lesion fiber image.

2. The system for demarcating suspected schistosomiasis lesion areas based on deep learning according to claim 1, characterized in that: The acquisition process of the lesion B-ultrasound image data includes: The detection B-ultrasound image acquisition module is equipped with six multi-parameter B-ultrasound devices controlled by free mechanical arms, each of which has a built-in convex array probe and a high-frequency linear array probe; Before B-ultrasound image acquisition, the convex array probe in each multi-parameter B-ultrasound device first scans the patient's entire liver area and simultaneously generates a corresponding three-dimensional volume image of the liver; The image features of the schistosomiasis lesion area are obtained through the Internet, and then the suspicious lesion area is traversed in the three-dimensional volume image of the liver according to the image features. Then, based on the distribution of the suspicious lesion area in the three-dimensional volume image of the liver in the patient's liver area at various shooting angles, various multi-parameter B-ultrasound devices perform multi-angle composite scans on the patient through high-frequency linear array probes, and then take a number of lesion B-ultrasound image data at various shooting angles in chronological order.

3. The deep learning-based schistosomiasis suspected lesion area calibration system according to claim 2, characterized in that: The shooting angles of the multi-angle composite scan include the subxiphoid sagittal section, the left subcostal transverse section, the right subcostal section, the right intercostal oblique section, and the high-frequency linear array probe parallel to and oblique to the long axis of the liver.

4. The system for demarcating suspected schistosomiasis lesion areas based on deep learning according to claim 3, characterized in that: The process of extracting the structural channel features includes: Gray-scale processing is performed on each lesion B-ultrasound image data, and a corresponding curvature feature matrix is ​​established according to each gray-scale pixel in the gray-scaled lesion B-ultrasound image data; According to the curvature feature matrix of each grayscale pixel, eigenvalue decomposition is performed to obtain its corresponding characteristic response value, and a characteristic response value threshold is set. The grayscale pixels whose characteristic response values ​​are less than or equal to the characteristic response value threshold are eliminated, and the grayscale pixels whose characteristic response values ​​are less than or equal to the characteristic response value threshold are retained, thereby segmenting the suspicious lesion area in the B-ultrasound image data of each lesion.

5. The deep learning-based schistosomiasis suspected lesion area calibration system according to claim 4, characterized in that: The process of extracting the texture channel features includes: Set the texture detection path and texture inspection angle, and generate the gray level co-occurrence matrix corresponding to the suspicious lesion area from the texture inspection angle according to the texture detection path; According to the grayscale matrix of each grayscale element, the energy value of each grayscale element is obtained, and the energy value of each grayscale element is mapped to the suspicious lesion area in the form of point cloud distribution, and then the energy threshold is set according to the energy value of the liver tissue under normal conditions. For the grayscale element in the suspicious lesion area whose energy value is less than the energy threshold, the corresponding grayscale element is recorded as a suspicious lesion element, otherwise the corresponding grayscale element is ignored; Setting multiple energy value level intervals, and setting the same label for suspicious lesion elements in the same energy value level interval, connecting adjacent suspicious lesion elements with the same label in the suspicious lesion area in sequence, and then dividing a number of texture boundary lines in the suspicious lesion area; The grayscale pixels between two texture boundary lines on adjacent plane positions are marked, and then several graded fibrosis curves are drawn in the suspicious lesion area.

6. The deep learning-based schistosomiasis suspected lesion area calibration system according to claim 5, characterized in that: The process of extracting dynamic channel features includes: The suspicious lesion areas corresponding to several lesion B-ultrasound image data taken at the same shooting angle are arranged in chronological order, the maximum width of each grade fibrosis curve in the suspicious lesion area is obtained, and the time series displacement value between the same grayscale pixel in the grade fibrosis curve in the adjacent time sequence is obtained, and the maximum value of the time series displacement value is counted; Then, a dynamic tracking window is established with the maximum width set as the length and the maximum value of the time series displacement value as the width, and then the displacement change position of the grayscale pixel in each level fiberization curve in the time sequence is captured in sequence through the dynamic tracking window, and the corresponding displacement change vector is generated.

7. The deep learning-based schistosomiasis suspected lesion area calibration system according to claim 6, characterized in that: The process of data image fusion of lesion B-ultrasound image data includes: According to the shooting angle and time sequence of each lesion B-ultrasound image data, the suspicious lesion area in each lesion B-ultrasound image data is input into the multi-layer image detection network from six directions and time sequence; A number of dynamic detection neurons are set according to the number of grayscale pixels in the suspicious lesion area, and a number of image detection time points are set, and then each time an image detection time point ends, the dynamic detection neuron moves to a corresponding position at the next image detection time point according to the displacement change vector contained in the grayscale element contained in it; At the same time, each shooting angle is taken as the main shooting angle, and the remaining shooting angles are taken as secondary shooting angles, so that the dynamic detection neurons of the main shooting angle do not perform any operation, and the remaining dynamic detection neurons of the secondary shooting angle are transformed according to the angle between the secondary shooting angle and the dynamic detection neurons of the main shooting angle, thereby generating dynamic lesion fiber images under different shooting angles.

8. The deep learning-based schistosomiasis suspected lesion area calibration system according to claim 7, characterized in that: The process of detecting the fusion results of the lesion B-ultrasound image data includes: A pixel difference threshold is set, and the pixel values ​​of the grayscale pixels corresponding to the same position and the same time sequence are compared with each other. If the pixel difference between the pixel values ​​of each grayscale pixel is below the pixel difference threshold, the pixel values ​​of the grayscale pixels corresponding to the same position and the same time sequence are directly accumulated and averaged, and then reassigned to each grayscale pixel; Otherwise, the grayscale pixels whose pixel difference between two grayscale pixels is greater than or equal to the pixel difference threshold are eliminated, the pixel values ​​of the retained grayscale pixels are accumulated and averaged, and then each grayscale pixel is reassigned to obtain a multi-view dynamic lesion fiber image.

Images