Kidney wound intelligent segmentation system

Abstract

The invention provides a kidney wound intelligent segmentation system, and relates to the technical field of image data processing. The kidney wound intelligent segmentation system comprises an image enhancement module; an edge fuzzy index acquisition module; a wound rule index acquisition module; and a wound segmentation index acquisition module. According to the method, the edge fuzzy index is obtained by combining the obtained kidney image edge data and the preset reference image edge data, and then the wound rule index is obtained according to the obtained kidney wound rule data and the preset reference wound rule data; and finally, the wound segmentation index is obtained by combining the obtained wound segmentation data and the preset reference segmentation data, so that the effect of improving the kidney wound segmentation accuracy is achieved, and the problem of low association degree of kidney wound segmentation and wound edge in the prior art is solved.

Description

Technical Field

[0001] The invention relates to the technical field of image data processing, and in particular to an intelligent renal wound surface segmentation system. Background Art

[0002] Intelligent segmentation of renal wounds is an important research direction in the field of medical image processing, especially in clinical applications such as renal wound suture reconstruction and trauma treatment. With the continuous advancement of medical technology, renal surgery is becoming increasingly minimally invasive and precise. Traditional surgical methods often rely on the doctor's experience and intuition, while intelligent segmentation of renal wounds based on surgical video images can provide doctors with more accurate and objective surgical guidance. Through accurate renal wound segmentation, doctors can more accurately understand the degree, scope and nature of renal damage, so as to formulate more scientific and reasonable treatment plans. The application prospects of renal wound segmentation are broad. In clinical practice, it can provide doctors with more objective and accurate diagnostic basis and treatment plans; in scientific research, it can provide important data support for the study of renal diseases; in medical education, it can be used as a teaching tool to help students better understand renal anatomy and pathological changes. The identification of renal wounds can help doctors more accurately assess the condition and formulate wound reconstruction strategies for surgical planning.

[0003] Existing systems mainly implement segmentation based on the pixel values ​​or feature differences at the boundaries between different regions in the image. The boundaries are usually manifested as mutations in local image features such as pixel grayscale, texture, color, etc. By detecting these mutation information, effective image segmentation can be achieved.

[0004] However, in the process of implementing the technical solution of the present invention, the present invention finds that the above technology has at least the following technical problems:

[0005] In the prior art, since the boundary of the kidney wound area may be fuzzy or irregular, the kidney wound segmentation has a low correlation with the wound edge. Summary of the invention

[0006] The embodiment of the present invention solves the problem in the prior art that the renal wound surface segmentation and the wound surface edge are not highly correlated by providing a renal wound surface intelligent segmentation system, thereby improving the accuracy of renal wound surface segmentation.

[0007] The embodiment of the present invention provides a renal wound intelligent segmentation system, comprising: an image enhancement module, an edge fuzzy index acquisition module, a wound rule index acquisition module and a wound segmentation index acquisition module: wherein the image enhancement module is used to acquire a renal wound image through a preset medical imaging device, and perform image enhancement processing on the renal wound image to acquire a characteristic wound image and renal image edge data; the edge fuzzy index acquisition module is used to obtain an edge fuzzy index based on the acquired renal image edge data and preset reference image edge data acquired from a preset database, and determine whether to perform image adjustment in combination with a preset edge threshold value acquired from the preset database, and if image adjustment is not performed, execute the function of the wound rule index acquisition module, and the edge fuzzy index is used to evaluate the characteristic wound image The wound rule index acquisition module is used to obtain the wound rule index according to the acquired kidney wound rule data and the preset reference wound rule data obtained from the preset database, and judge whether to execute the wound segmentation index acquisition module function in combination with the preset wound rule threshold obtained from the database, and the wound rule index is used to evaluate the edge rule situation of the characteristic wound image; the wound segmentation index acquisition module is used to segment the characteristic wound image to obtain the segmented wound image and wound segmentation data, and obtain the wound segmentation index in combination with the preset reference segmentation data obtained from the preset database, and perform segmentation adjustment based on the wound segmentation index and the preset segmentation threshold obtained from the preset database, and the wound segmentation index is used to evaluate the segmentation situation of the characteristic wound image.

[0008] Optionally, the specific process of acquiring the characteristic wound image is as follows: scanning the kidney area with a preset medical imaging device to acquire a kidney wound image, the preset medical imaging device includes a computer tomography scan, a magnetic resonance imaging and an ultrasound imaging device; performing image enhancement processing on the kidney wound image to acquire a characteristic wound image, the image enhancement processing includes contrast enhancement, edge enhancement and regional enhancement.

[0009] Optionally, the specific acquisition process of the edge blur index based on the acquired kidney image edge data and the preset reference image edge data obtained from a preset database is as follows: edge data extraction is performed on the characteristic wound image to obtain kidney image edge data, the kidney image edge data includes a first grayscale value, a second grayscale value, and a pixel distance, and the kidney image edge data is used to describe the edge contour of the kidney wound in the characteristic wound image; a preset gradient threshold and a preset grayscale value change rate threshold are obtained from the preset database, and an edge gradient amplitude deviation and a grayscale value change rate deviation are obtained in combination with the kidney image edge data; a preset gradient deviation threshold and a preset grayscale value change rate deviation threshold are obtained from the preset database, and an edge blur index is obtained in combination with the edge gradient amplitude deviation and the grayscale value change rate deviation.

[0010] Optionally, the edge blur index is calculated using the following formula:

[0011]

[0012] In the formula, α i represents the edge blur index of the i-th pixel on the characteristic wound surface image, i=1,2,...,n-1, i represents the number of the pixel on the characteristic wound surface image, n represents the total number of pixels on the characteristic wound surface image, R i G represents the gray value change rate deviation of the i-th pixel on the characteristic wound image. i Indicates the edge gradient amplitude deviation of the i-th pixel on the characteristic wound surface image, I i represents the first gray value of the i-th pixel on the characteristic wound surface image, Ii' represents the second gray value of the i-th pixel on the characteristic wound surface image, ΔX i represents the pixel distance corresponding to the i-th pixel on the characteristic wound image, G0 represents the preset gradient threshold, R0 represents the preset gray value change rate threshold, ΔR represents the preset gray value change rate deviation threshold, ΔG represents the preset gradient deviation threshold, It represents the first-order derivative of the gray value corresponding to the i-th pixel in the x-axis direction. It represents the first-order derivative of the gray value corresponding to the i-th pixel in the y-axis direction, and e represents a natural constant.

[0013] Optionally, the specific process of determining whether to perform image adjustment in combination with a preset edge threshold obtained from a preset database is as follows: Q1, determining whether the edge blur index is not higher than a preset edge threshold, and not performing image adjustment when the edge blur index is not higher than the preset edge threshold, otherwise executing Q2; Q2, sharpening the characteristic wound image, and ending image adjustment when the monitored edge blur index is not higher than the preset edge threshold, otherwise executing Q3; Q3, removing noise from the characteristic wound image, and ending image adjustment when the monitored edge blur index is not higher than the preset edge threshold, otherwise the system prompts the preset personnel to re-acquire the kidney wound image.

[0014] Optionally, the specific process of acquiring the kidney wound surface regular data is as follows: acquiring the kidney wound surface regular data through a preset image measurement device, the kidney wound surface regular data including tangent angle, curve arc length, and edge point distance.

[0015] Optionally, the specific acquisition process of the wound regularity index is as follows: obtain a preset curvature deviation threshold from a preset database, and obtain the curvature influence by combining the preset curvature threshold, kidney wound regularity data and the preset curvature deviation threshold; obtain a preset distance standard deviation deviation threshold from a preset database, and obtain the distance standard deviation influence by combining the preset distance standard deviation threshold, kidney wound regularity data and the preset distance standard deviation deviation threshold; obtain preset weights from a preset database, and obtain the wound regularity index by combining the obtained curvature influence and distance standard deviation influence, wherein the preset weights include a preset regularity first weight and a preset regularity second weight.

[0016] Optionally, the specific process of performing segmentation processing on the characteristic wound image to obtain the segmented wound image and wound segmentation data is as follows: the wound segmentation data is obtained through a three-dimensional slicer, and the wound segmentation data includes the intersection area, the union area, the number of positive pixels, and the number of labeled positive pixels. The intersection area represents the area of ​​the overlapping part of the wound area in the segmented wound image and the manually annotated wound area, and the union area represents the area of ​​the covered part after the wound area in the segmented wound image and the manually annotated wound area are combined.

[0017] Optionally, the specific acquisition process of the wound segmentation index is as follows: obtaining a preset intersection-and-union ratio threshold from a preset database, and obtaining the preset intersection-and-union ratio threshold and the difference between the ratio of the intersection area and the union area by combining the intersection area and the union area; obtaining a preset segmentation accuracy threshold from a preset database, and obtaining the preset segmentation accuracy threshold and the difference between the ratio of the number of positive pixels and the number of labeled positive pixels by combining the number of positive pixels and the number of labeled positive pixels; obtaining a first segmentation weight and a preset second segmentation weight from a preset database, and combining the preset intersection-and-union ratio threshold and the difference between the ratio of the intersection area and the union area, the preset segmentation accuracy threshold and the difference between the ratio of the number of positive pixels and the number of labeled positive pixels to obtain the wound segmentation index; the preset reference segmentation data includes a preset segmentation accuracy threshold, a preset intersection-and-union ratio threshold, a preset first segmentation weight, and a preset second segmentation weight, and the wound segmentation index is obtained in combination with the wound segmentation data.

[0018] Optionally, the specific process of performing segmentation adjustment based on the wound segmentation index and a preset segmentation threshold obtained from a preset database is as follows: M1, determining whether the wound segmentation index is not lower than a preset segmentation threshold. When the wound segmentation index is not lower than the preset segmentation threshold, no segmentation adjustment is performed, otherwise M2 is continued to be executed; M2, obtaining the preset segmentation threshold from the preset database, sending a prompt to the preset personnel to gradually reduce the segmentation area by a preset multiple of the preset segmentation threshold, and ending the segmentation adjustment when the monitored wound segmentation index is not lower than the preset segmentation threshold, otherwise M3 is continued to be executed; M3, performing area merging, ending the segmentation adjustment when the monitored wound segmentation index is not lower than the preset segmentation threshold, otherwise feedback is given to the preset personnel to re-acquire the kidney wound image.

[0019] One or more technical solutions provided in the embodiments of the present invention have at least the following technical effects or advantages:

[0020] 1. The edge fuzzy index is obtained through the kidney image edge data and the preset reference image edge data, and then the wound rule index is obtained according to the obtained kidney wound rule data and the preset reference wound rule data. Finally, the wound segmentation index is obtained by combining the obtained wound segmentation data and the preset reference segmentation data, thereby achieving a comprehensive improvement in the acquisition of kidney wound segmentation data, and then achieving an improvement in the accuracy of kidney wound segmentation, effectively solving the problem of low correlation between kidney wound segmentation and wound edge in the prior art.

[0021] 2. Determine whether to adjust the image by using the preset edge threshold and edge blur index, and then determine whether to execute the wound segmentation index acquisition module function by combining the preset wound rule threshold and wound rule index. Finally, perform segmentation adjustment based on the wound segmentation index and the preset segmentation threshold, thereby achieving comprehensive adjustment and improving the reliability of kidney wound area segmentation.

[0022] 3. The edge data of the kidney image is obtained by extracting the edge data of the characteristic wound image, and then the kidney wound rule data is obtained through the preset image measurement equipment. Finally, the wound segmentation data is obtained through the three-dimensional slicer, thereby achieving the improvement of the real-time performance of obtaining the kidney wound segmentation data, and then achieving the improvement of the accuracy of obtaining the kidney wound segmentation data. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic diagram of the structure of a kidney wound intelligent segmentation system provided by an embodiment of the present invention;

[0025] Figure 2 A statistical graph showing changes in the wound surface regularity index provided by an embodiment of the present invention;

[0026] Figure 3 A postoperative renal wound image provided by an embodiment of the present invention;

[0027] Figure 4 A kidney image before wound surface segmentation provided by an embodiment of the present invention;

[0028] Figure 5A kidney image after wound surface segmentation provided by an embodiment of the present invention;

[0029] Figure 6 A surgical procedure splitting framework diagram provided by an embodiment of the present invention;

[0030] Figure 7 A surgical stage diagram provided by an embodiment of the present invention;

[0031] Figure 8 A diagram of surgical steps provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] Unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "one", "one" or "the" do not indicate a quantitative limitation, but indicate the existence of at least one. Words such as "include" or "comprise" mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connect" or "connected" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.

[0034] It should be noted that the terms "up", "down", "left", "right", "front", "back", etc. used in the present invention are only used to indicate relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0035] The embodiment of the present invention solves the problem in the prior art that renal wound segmentation and wound edge are not highly correlated by providing a renal wound intelligent segmentation system. A renal wound image is acquired by a preset medical imaging device, and then an edge blur index is obtained based on the renal image edge data and the preset reference image edge data, and it is determined whether to perform image adjustment. Then, a wound rule index is obtained based on the acquired renal wound rule data and the preset reference wound rule data, and it is determined whether to execute the wound segmentation index acquisition module function. Finally, the wound segmentation index is obtained by combining the acquired wound segmentation data and the preset reference segmentation data, and segmentation adjustment is performed, thereby improving the accuracy of renal wound segmentation.

[0036] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0037] like Figure 1As shown, it is a structural schematic diagram of a renal wound surface intelligent segmentation system provided by an embodiment of the present invention, comprising: an image enhancement module, an edge fuzzy index acquisition module, a wound surface rule index acquisition module and a wound surface segmentation index acquisition module: wherein the image enhancement module is used to acquire a renal wound surface image through a preset medical imaging device, and perform image enhancement processing on the renal wound surface image to acquire a characteristic wound surface image and renal image edge data, the image enhancement processing is used to enhance the wound surface characteristics of the renal wound surface image, the characteristic wound surface image represents the renal wound surface image after image enhancement processing, and the renal image edge data is used to describe the edge characteristics of the characteristic wound surface image; the edge fuzzy index acquisition module is used to obtain an edge fuzzy index according to the acquired renal image edge data and the preset reference image edge data obtained from a preset database, and determine whether to perform image adjustment in combination with a preset edge threshold value obtained from the preset database, if image adjustment is not performed, the wound surface rule index acquisition module function is executed, and the edge fuzzy index is used to evaluate the edge fuzziness degree of the characteristic wound surface image, The impact adjustment is used to adjust the edge blur index to no higher than the preset edge threshold; the wound rule index acquisition module is used to obtain the wound rule index based on the acquired kidney wound rule data and the preset reference wound rule data obtained from the preset database, and determine whether to execute the wound segmentation index acquisition module function in combination with the preset wound rule threshold obtained from the database. The wound rule index is used to evaluate the edge rule situation of the characteristic wound image; the wound segmentation index acquisition module is used to segment the characteristic wound image to obtain the segmented wound image and wound segmentation data, and obtain the wound segmentation index in combination with the preset reference segmentation data obtained from the preset database, and perform segmentation adjustment based on the wound segmentation index and the preset segmentation threshold obtained from the preset database. The segmented wound image represents the characteristic wound image after segmentation processing. The wound segmentation index is used to evaluate the segmentation situation of the characteristic wound image. The segmentation processing is used to segment the characteristic wound image through the image segmentation algorithm. The segmentation adjustment is used to adjust the wound segmentation index to no lower than the preset segmentation threshold.

[0038] In this embodiment, the edge blur index is used to evaluate the edge blur of the characteristic wound image. If the edge blur index is higher than the preset edge threshold, image adjustment may be required to improve the image quality and make its edges clearer. The wound rule index is used to evaluate the edge rule of the characteristic wound image. If the wound rule index is lower than the preset wound rule threshold, further processing is required to improve the segmentation result. The wound segmentation index is used to evaluate the segmentation of the characteristic wound image. The segmentation adjustment ensures that the wound segmentation index is not lower than the preset segmentation threshold to ensure the accuracy of the segmentation. These three indexes are interrelated and work together in the process of kidney segmentation. The accuracy of segmentation can be evaluated by the edge fuzzy index and the wound rule index, and the wound segmentation index is a direct indicator for evaluating the segmentation effect. The preset reference image edge data includes a preset gradient threshold, a preset gray value change rate threshold, a preset gradient deviation threshold and a preset gray value change rate deviation threshold. The preset reference wound rule data includes a preset curvature threshold, a preset distance standard deviation threshold, a preset curvature deviation threshold and a preset distance standard deviation deviation threshold. The preset reference segmentation data includes a preset segmentation accuracy threshold and a preset intersection-over-union threshold. The preset reference image edge data, preset reference wound rule data and preset reference segmentation data are obtained from the preset database; the accuracy of renal wound segmentation is improved.

[0039] It should be understood that after image adjustment, after obtaining an edge blur index that is not higher than the preset edge threshold, the wound rule index module acquisition function continues to be executed; the preset wound rule threshold obtained from the database is used to determine whether to execute the wound segmentation index acquisition module function, and when the wound rule index is not lower than the preset wound rule threshold, the wound segmentation index acquisition module function continues to be executed; the segmentation adjustment is performed to obtain a wound segmentation index that is not lower than the preset segmentation threshold.

[0040] Optionally, the specific process of acquiring the characteristic wound image is as follows: scanning the kidney area with a preset medical imaging device to acquire a kidney wound image, the preset medical imaging device includes a computer tomography scan, a magnetic resonance imaging and an ultrasound imaging device; performing image enhancement processing on the kidney wound image to acquire a characteristic wound image, the image enhancement processing includes contrast enhancement, edge enhancement and regional enhancement, contrast enhancement means enhancing the contrast of the kidney wound image by a histogram equalization method, edge enhancement means highlighting the edge contour of the kidney wound in the kidney wound image by a Sobel edge detection algorithm, regional enhancement means enhancing the preset kidney wound area features by a local histogram equalization method, and a rectangular coordinate system has been added to the characteristic wound image.

[0041] In this embodiment, the contrast of the renal wound image is enhanced by the histogram equalization method, so that the grayscale difference between different tissues in the image is more obvious, which is helpful for subsequent edge detection and regional segmentation. The renal wound image is converted into a grayscale image, and the histogram equalization function in the image processing library is used to calculate the histogram of the input grayscale image so that the histogram distribution of the image is uniform, thereby improving the contrast of the image. The Sobel edge detection algorithm is used to highlight the edge contour of the renal wound in the renal wound image, and the image is convoluted in the horizontal and vertical directions using the Sobel operator to calculate the gradient amplitude. Edge enhancement helps to clarify the boundary of the kidney, which is very important for the segmentation algorithm because accurate edge information can guide the segmentation algorithm to more accurately identify the target area. The local histogram equalization method enhances the preset renal wound area features, applies histogram equalization to each local area (such as the renal wound area), performs histogram equalization on each block, and then uses methods such as bilinear interpolation to smooth the transition between blocks, and merges the processed local area back into the original image to form a complete enhanced image. Regional enhancement helps to improve the contrast and details in a specific area, allowing the segmentation algorithm to better distinguish different tissues and structures inside the kidney. In the field of renal wound segmentation, contrast enhancement provides a better basis for edge detection, and the results of edge detection provide guidance for regional enhancement, so that regional enhancement can be more accurately applied to the required areas, thus improving the accuracy of renal wound segmentation.

[0042] Optionally, the specific acquisition process of the edge blur index based on the acquired kidney image edge data and the preset reference image edge data obtained from a preset database is as follows: edge data extraction is performed on the characteristic wound image to obtain kidney image edge data, the kidney image edge data includes a first grayscale value, a second grayscale value, and a pixel point distance, the edge data extraction means extracting the kidney image edge data in the characteristic wound image through the Sobel edge detection algorithm, the kidney image edge data is used to describe the edge contour of the kidney wound in the characteristic wound image, the first grayscale value represents the grayscale value of the pixel point on the characteristic wound image, and the second grayscale value represents the adjacent pixels of the pixel point corresponding to the first grayscale value. The grayscale value of the point is represented by the pixel point distance, and the pixel point distance represents the distance between the pixel points corresponding to the first grayscale value and the second grayscale value; the preset gradient threshold and the preset grayscale value change rate threshold are obtained from the preset database, and the edge gradient amplitude deviation and the grayscale value change rate deviation are obtained in combination with the kidney image edge data. The grayscale value change rate deviation represents the difference between the preset grayscale value change rate threshold and the grayscale value change rate on the characteristic wound surface image, and the edge gradient amplitude deviation represents the difference between the preset gradient threshold and the edge gradient amplitude on the characteristic wound surface image; the preset gradient deviation threshold and the preset grayscale value change rate deviation threshold are obtained from the preset database, and the edge blur index is obtained in combination with the edge gradient amplitude deviation and the grayscale value change rate deviation.

[0043] The edge blur index is calculated using the following formula:

[0044]

[0045]

[0046] In the formula, α i represents the edge blur index of the i-th pixel on the characteristic wound surface image, i=1,2,...,n-1, i represents the number of the pixel on the characteristic wound surface image, n represents the total number of pixels on the characteristic wound surface image, R i G represents the gray value change rate deviation of the i-th pixel on the characteristic wound image. i Indicates the edge gradient amplitude deviation of the i-th pixel on the characteristic wound surface image, I i Represents the first gray value of the i-th pixel on the characteristic wound surface image, I i‘ Indicates the second gray value of the i-th pixel on the characteristic wound image, ΔX i represents the pixel distance corresponding to the i-th pixel on the characteristic wound image, G0 represents the preset gradient threshold, R0 represents the preset gray value change rate threshold, ΔR represents the preset gray value change rate deviation threshold, ΔG represents the preset gradient deviation threshold, It represents the first-order derivative of the gray value corresponding to the i-th pixel in the x-axis direction. It represents the first-order derivative of the gray value corresponding to the i-th pixel in the y-axis direction, and e represents a natural constant.

[0047] In this embodiment, the preset gradient threshold is represented by directly obtaining the maximum value of the edge gradient data of the historical period in the preset database, the preset gray value change rate threshold is represented by directly obtaining the maximum value of the gray value change rate data of the historical period in the preset database, the preset gradient deviation threshold is represented by directly obtaining the average value of the gradient deviation data of the historical period in the preset database, and the preset gray value change rate deviation threshold is represented by directly obtaining the average value of the gray value change rate deviation data of the historical period in the preset database. In the field of renal image segmentation, the interaction of the three parameters of the first gray value, the second gray value and the pixel distance is crucial for accurately describing the edge contour of the renal wound. The first gray value represents the gray value of the pixel on the characteristic wound image, which reflects the brightness information of the pixel in the image. The second gray value represents the gray value of the adjacent pixel, and the adjacent pixel represents the pixel with the smallest distance from the pixel corresponding to the first gray value. The comparison between it and the first gray value can reveal the brightness change between pixels, which is often an indication of the existence of an edge. The pixel distance provides the spatial distribution information of these changes, which helps to locate the edge more accurately; the accuracy of renal wound segmentation is improved.

[0048] Specifically, the algorithm of this embodiment combines the kidney image edge data for comprehensive analysis to obtain the edge blur index. In this formula, there is a common regulatory association between the kidney image edge data. For example, the difference between the first grayscale value and the second grayscale value reflects the degree of grayscale change at the edge. The first grayscale value, the second grayscale value and the pixel distance jointly determine the accuracy and continuity of the edge. When the pixel distance increases, this spatial correlation weakens, and the difference in grayscale values ​​between adjacent pixels increases. As the second grayscale value and the first grayscale value increase, when the increase in the first grayscale value is less than the second grayscale value, the edge blur index decreases, which means that the second grayscale value is inversely proportional to the edge blur index. The grayscale value change rate reflects the severity of the grayscale change at the edge. The smaller the change rate, the more blurred the edge. The grayscale value change rate reflects the degree of difference in the grayscale values ​​of pixels on both sides of the edge, and the edge gradient amplitude is a quantitative representation of this difference in the edge direction.

[0049] Specifically, assuming that the gray value change rate deviation R i The range is 0-10, the edge gradient amplitude deviation G i The range is 0-50, the preset gray value change rate deviation threshold ΔR is 5, the preset gradient deviation threshold ΔG is 25, and the edge blur index change statistics are shown in Table 1:

[0050] Table 1 Statistics of changes in edge blur index

[0051] <![CDATA[Deviation R of gray value change rate i > <![CDATA[Edge gradient magnitude deviation G i > <![CDATA[Edge blur index α i > 8.66 46.02 2.6627 5.83 38.79 2.2847 2.33 26.26 1.6735 ... ... ...

[0052] It can be seen from Table 1 that as the gray value change rate deviation R i and edge gradient amplitude deviation G i The edge blur index gradually decreases, which means that the edge blur of the characteristic wound image gradually decreases. In kidney wound segmentation, accurate boundary recognition is the key to improving segmentation accuracy. Clear edge information is crucial for determining the location and shape of the wound; the accuracy of kidney wound segmentation is improved.

[0053] Optionally, the specific process of determining whether to perform image adjustment in combination with a preset edge threshold obtained from a preset database is as follows: Q1, determining whether the edge blur index is not higher than the preset edge threshold. When the edge blur index is not higher than the preset edge threshold, no image adjustment is performed, otherwise Q2 is executed; Q2, sharpening the characteristic wound image. When the monitored edge blur index is not higher than the preset edge threshold, the image adjustment is terminated. Otherwise, Q3 is executed. The sharpening process is used to enhance the edge information in the characteristic wound image to improve clarity; Q3, noise removal is performed on the characteristic wound image. When the monitored edge blur index is not higher than the preset edge threshold, the image adjustment is terminated. Otherwise, the system prompts the preset personnel to re-acquire the kidney wound image. The noise removal process is used to reduce and eliminate random noise in the characteristic wound image.

[0054] In this embodiment, the preset edge threshold is represented by directly obtaining the average value of the grayscale variance data of the historical period in the preset database. The purpose of the sharpening process is to enhance the edge information in the characteristic wound image to improve the clarity of the image. The noise removal process is to reduce and eliminate the random noise in the characteristic wound image. Noise may interfere with the recognition of the edge by the segmentation algorithm, resulting in inaccurate segmentation results. Gaussian filtering is a linear smoothing filter suitable for removing Gaussian noise. Median filtering is a nonlinear filtering technology, particularly suitable for removing salt and pepper noise. In the field of renal wound segmentation, image enhancement processing is a key step to improve segmentation accuracy. The renal wound image obtained by the preset medical imaging device can significantly improve the quality of the characteristic wound image after image enhancement processing, thereby improving the accuracy and efficiency of segmentation. The characteristic wound image is read using the image processing library, a sharpening kernel is created and convolved with the image, and the edge information can be highlighted. The Laplace operator is a second-order derivative operator commonly used for image sharpening. It can enhance the edge part of the image to make the image look clearer; remove salt and pepper noise by median filtering, and remove Gaussian noise by Gaussian filtering or bilateral filtering; and improve the accuracy of renal wound segmentation.

[0055] Optionally, the specific acquisition process of obtaining the wound rule index based on the acquired renal wound rule data and the preset reference wound rule data obtained from the preset database is as follows: the renal wound rule data is obtained by a preset image measuring device, the renal wound rule data includes the tangent angle, the arc length of the curve, and the edge point distance, the preset image measuring device includes a CT scanner and a three-dimensional slicer, and the edge point distance represents the distance between the edge point marked on the edge curve on the characteristic wound image and the center point of the preset edge curve; the preset curvature threshold and the preset distance standard deviation threshold are obtained from the preset database, and the curvature deviation and the distance standard deviation deviation are obtained in combination with the renal wound rule data, the curvature deviation represents the difference between the preset curvature threshold and the curvature of the edge curve in the characteristic wound image, and the distance standard deviation deviation represents the distance between the edge curve in the characteristic wound image and the curvature. The difference between the preset distance standard deviation threshold and the distance standard deviation of the edge curve; obtaining the preset curvature deviation threshold and the preset distance standard deviation deviation threshold from the preset database, and combining the curvature deviation and the distance standard deviation deviation to obtain the curvature influence and the distance standard deviation influence, the curvature influence is used to evaluate the influence of the curvature deviation on the wound regularity index, and the distance standard deviation influence is used to evaluate the influence of the distance standard deviation deviation on the wound regularity index; obtaining the preset regularity first weight and the preset regularity second weight from the preset database, and obtaining the wound regularity index by combining the curvature influence and the distance standard deviation influence, the preset regularity first weight is used to evaluate the influence of the curvature influence on the wound regularity index, and the preset regularity second weight is used to evaluate the influence of the distance standard deviation influence on the wound regularity index.

[0056] The wound regularity index is calculated using the following formula:

[0057] β f =tanh(k f *ρ1+D f *ρ2);

[0058]

[0059] In the formula, β f represents the wound regularity index of the edge curve corresponding to the fth wound on the characteristic wound image, f = 1, 2, ..., b, f represents the wound number on the characteristic wound image, b represents the total number of wounds on the characteristic wound image, k f represents the curvature influence of the edge curve corresponding to the fth wound surface on the characteristic wound surface image, D f Indicates the influence of the distance standard deviation of the edge curve corresponding to the fth wound surface on the characteristic wound surface image, θ vf represents the tangent angle corresponding to the vth annotated edge point on the edge curve corresponding to the fth wound surface on the characteristic wound surface image, v = 1, 2, ..., a, v represents the number of the annotated edge point on the edge curve, a represents the total number of annotated edge points on the edge curve, Sf The arc length of the edge curve corresponding to the fth wound surface on the characteristic wound surface image, d vf represents the edge point distance corresponding to the vth marked edge point on the edge curve corresponding to the fth wound on the characteristic wound image, k0 represents the preset curvature threshold, d0 represents the preset distance standard deviation threshold, Δk represents the preset curvature deviation threshold, Δd represents the preset distance standard deviation deviation threshold, ρ1 represents the preset regularity first weight, ρ2 represents the preset regularity second weight, express About S f The first derivative of , e represents a natural constant.

[0060] In this embodiment, a preset curvature deviation threshold is obtained from a preset database, and the curvature influence is obtained by combining the preset curvature threshold, kidney wound rule data and the preset curvature deviation threshold; a preset distance standard deviation deviation threshold is obtained from the preset database, and the distance standard deviation influence is obtained by combining the preset distance standard deviation threshold, kidney wound rule data and the preset distance standard deviation deviation threshold; a preset weight is obtained from the preset database, and the wound rule index is obtained by combining the obtained curvature influence and distance standard deviation influence, and the preset weight includes a preset regularity first weight and a preset regularity second weight. The preset curvature threshold is represented by directly obtaining the maximum value of the curvature data of the historical time period in the preset database, the preset distance standard deviation threshold is represented by directly obtaining the maximum value of the distance standard deviation data of the historical time period in the preset database, the preset curvature deviation threshold is represented by directly obtaining the average value of the curvature deviation data of the historical time period in the preset database, and the preset distance standard deviation deviation threshold is represented by directly obtaining the average value of the distance standard deviation deviation data of the historical time period in the preset database. A CT scanner is used to obtain kidney wound images, and a three-dimensional slicer is used to reconstruct the image. An edge detection algorithm is used to extract edges, and the direction of the tangent is calculated at each point of the edge curve. The tangent angle can be calculated by taking the angle between the tangent direction and the horizontal line, and the arc length of the curve is obtained by accumulating the distances between the edge points; thereby improving the accuracy of kidney wound segmentation.

[0061] Specifically, the sum of the preset first regularity weight and the preset second regularity weight is 1, for example, the preset first regularity weight is 0.5, and the preset second regularity weight is 0.5; the preset first regularity weight is the weight corresponding to the curvature value preset in the preset database, which represents the influence degree of the curvature value on the wound regularity index. When used, the weight corresponding to the preset curvature value can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the curvature of the kidney wound segmentation training set and the weight corresponding to the curvature value preset in the preset database form a mapping set, and the real-time intersection area is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0062] The algorithm of this embodiment combines the kidney wound surface rule data for comprehensive analysis to obtain the wound surface rule index. In this formula, there is a common regulatory association between the kidney wound surface rule data. For example, the tangent angle reflects the rate of change of the direction of the edge curve at a specific point, and the arc length of the curve describes the length of the edge curve, which is directly related to the actual size and shape of the kidney wound surface. The change of the tangent angle will affect the measurement of the arc length of the curve, because the arc length is the result of integration along the edge curve, and the edge point distance is related to both the tangent angle and the arc length of the curve, because it describes the position of the edge point relative to the center point. As the tangent angle and the edge The increase of point distance and the decrease of curve arc length lead to an increase of wound surface regularity index, which means that the tangent angle and edge point distance are proportional to the wound surface regularity index. The larger the curvature deviation, the more irregular the edge curve shape, and the greater the impact on the wound surface regularity index. The larger the distance standard deviation deviation, the more uneven the edge point distribution, which will also increase the irregularity of the wound surface. When the edge curve shape is distorted and the edge point distribution is extremely uneven (the distance standard deviation deviation is large), the curvature deviation and the distance standard deviation deviation may increase at the same time, jointly aggravating the irregularity of the wound surface. The curve arc length is inversely proportional to the wound surface regularity index.

[0063] Specifically, assuming that the curvature influence k f The range is 0-2, and the distance standard deviation influences D f The range is 0-2, the first regularity weight ρ1 is preset to 0.5, and the second regularity weight ρ2 is preset to 0.5. Figure 2 As shown in FIG. 1 , it is a statistical diagram of changes in the wound surface regularity index provided by an embodiment of the present invention, such as Figure 2As shown in the figure, when the curvature influence is 2, the wound regularity index gradually increases with the increase of the distance standard deviation influence. When the distance standard deviation influence is 1, the wound regularity index gradually increases with the increase of the curvature influence, which means that the edge regularity of the characteristic wound image gradually improves. In the field of renal wound segmentation, by analyzing the changing trends of the curvature influence and the distance standard deviation influence, the boundary of the kidney can be more accurately identified and located, thereby improving the accuracy of renal wound segmentation.

[0064] Optionally, the characteristic wound image is segmented to obtain a segmented wound image and wound segmentation data, and a wound segmentation index is obtained in combination with preset reference segmentation data obtained from a preset database. The specific process is as follows: wound segmentation data is obtained through a three-dimensional slicer, and the wound segmentation data includes an intersection area, a union area, a number of positive pixels, and a number of labeled positive pixels. The intersection area represents the area of ​​the overlapping part of the wound area in the segmented wound image and the manually annotated wound area, and the union area represents the area of ​​the covered part after the wound area in the segmented wound image and the manually annotated wound area are combined; a preset segmentation accuracy threshold, a preset intersection-union ratio threshold, a preset first segmentation weight, and a preset second segmentation weight are obtained from a preset database, and the wound segmentation index is obtained in combination with the wound segmentation data. The preset first segmentation weight is used to evaluate the influence of the segmentation accuracy on the wound segmentation index, and the preset second segmentation weight is used to evaluate the influence of the intersection-union ratio on the wound segmentation index.

[0065] The wound segmentation index was calculated using the following formula:

[0066]

[0067] In the formula, γ p represents the wound segmentation index of the pth segmented wound image, p=1,2,...,m, p represents the number of the segmented wound image, m represents the total number of segmented wound images, S 1p represents the intersection area corresponding to the pth segmented wound image, S 2p represents the corresponding union area of ​​the pth segmented wound surface image, N 1p represents the number of positive pixels in the pth segmented wound image, N 2p represents the number of labeled positive pixels in the pth segmented wound image, S0 represents the preset intersection-over-union ratio threshold, N0 represents the preset segmentation accuracy threshold, ω1 represents the preset first segmentation weight, and ω2 represents the preset second segmentation weight.

[0068] In this embodiment, a preset intersection-and-union ratio threshold is obtained from a preset database, and the preset intersection-and-union ratio threshold and the difference between the ratio of the intersection area and the union area are obtained by combining the intersection area and the union area; a preset segmentation accuracy threshold is obtained from a preset database, and the preset segmentation accuracy threshold and the difference between the ratio of the number of positive pixels and the number of labeled positive pixels are obtained by combining the number of positive pixels and the number of labeled positive pixels; a first segmentation weight and a preset second segmentation weight are obtained from a preset database, and a wound segmentation index is obtained by combining the preset intersection-and-union ratio threshold and the difference between the ratio of the intersection area and the union area, the preset segmentation accuracy threshold and the difference between the ratio of the number of positive pixels and the number of labeled positive pixels; the preset reference segmentation data includes a preset segmentation accuracy threshold, a preset intersection-and-union ratio threshold, a preset first segmentation weight and a preset second segmentation weight, and the wound segmentation index is obtained in combination with the wound segmentation data. The preset accuracy threshold is represented by directly obtaining the maximum value of the kidney wound segmentation accuracy data of the historical period in the preset database, and the preset intersection-union ratio threshold is represented by directly obtaining the maximum value of the intersection-union ratio data of the kidney wound in the historical period in the preset database. The manually annotated wound image is obtained and segmented to obtain the segmented wound image. The two images are converted into binary images in a three-dimensional slicer, in which the wound area is 1 and the other areas are 0. The segmented wound image and the manually annotated wound image are bitwise "AND" operated, and the number of pixel values ​​​​of 1 in the result image is calculated to obtain the intersection area. The segmented wound image and the manually annotated wound image are bitwise "OR" operated, and the number of pixel values ​​​​of 1 in the result image is calculated to obtain the union area. The total number of pixel values ​​​​of 1 in the segmented wound image, that is, the number of positive pixels, is calculated. The total number of pixel values ​​​​of 1 in the manually annotated wound image, that is, the number of labeled positive pixels, is calculated; the accuracy of kidney wound segmentation is improved.

[0069] Specifically, the sum of the preset first segmentation weight and the preset second segmentation weight is 1, for example, the preset first segmentation weight is 0.5, and the preset second segmentation weight is 0.5; the preset first segmentation weight is the weight corresponding to the intersection area value preset in the preset database, which represents the influence degree of the intersection area value on the wound segmentation index. When used, the weight corresponding to the preset intersection area value can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the intersection area of ​​the renal wound segmentation training set and the weight corresponding to the intersection area value preset in the preset database form a mapping set, and the real-time intersection area is input into the mapping set to obtain the corresponding weight, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, its value range is [0, 1].

[0070] The algorithm of this embodiment combines the wound segmentation data for comprehensive analysis to obtain the wound segmentation index. In this formula, there is a common regulatory association between the wound segmentation data. For example, an increase in the intersection area usually means that more positive pixels are correctly segmented. In the segmentation result, if the pixels of the non-labeled area are incorrectly included, the union area will increase, and the precision will decrease. The increase in the number of positive pixels usually means that the segmentation algorithm has an enhanced ability to recognize the target area, which helps to improve the accuracy and completeness of the segmentation. In the wound segmentation index, the number of labeled positive pixels is used as a benchmark value to evaluate the completeness of the segmentation result relative to the true annotation. The intersection-union ratio and the precision are both important indicators for measuring segmentation performance. They evaluate the quality of the segmentation results from different angles. In order to improve the intersection-union ratio, the precision needs to be reduced to cover more real target areas. As the intersection area and the number of positive pixels increase, the union area and the number of labeled positive pixels decrease, and the wound segmentation index gradually increases, which means that the intersection area and the number of positive pixels are positively proportional to the wound segmentation index, and the union area and the number of labeled positive pixels are inversely proportional to the wound segmentation index; the accuracy of kidney wound segmentation is improved.

[0071] Optionally, the specific process of performing segmentation adjustment based on the wound segmentation index and a preset segmentation threshold obtained from a preset database is as follows: M1, determining whether the wound segmentation index is not lower than a preset segmentation threshold. When the wound segmentation index is not lower than the preset segmentation threshold, no segmentation adjustment is performed, otherwise M2 is continued to be executed; M2, obtaining the preset segmentation threshold from the preset database, sending a prompt to the preset personnel to gradually reduce the segmentation area by a preset multiple of the preset segmentation threshold, and ending the segmentation adjustment when the monitored wound segmentation index is not lower than the preset segmentation threshold, otherwise M3 is continued to be executed; M3, performing region merging, ending the segmentation adjustment when the monitored wound segmentation index is not lower than the preset segmentation threshold, otherwise feedback is given to the preset personnel to re-acquire the kidney wound image, and region merging means reducing the degree of discontinuity of the segmented area through a region merging algorithm.

[0072] In this embodiment, the preset segmentation threshold is represented by the average value of the segmentation data in the preset database, and the preset segmentation threshold is represented by the average value of the wound segmentation index data in the preset database. When the wound segmentation index is not lower than the preset segmentation threshold, it means that the current segmentation effect has reached an acceptable level and the operation stops. If the wound segmentation index is still lower than the threshold, it means that the segmentation effect is not good. Regional merging is a process of reducing the degree of discontinuity of the segmented region through the regional merging algorithm, which helps to improve the coherence and integrity of the segmented region. The interaction between these two steps is that they together constitute an iterative process, which gradually adjusts the segmented region and regional merging to improve the accuracy of kidney wound segmentation.

[0073] like Figure 3As shown, this is a postoperative renal wound image provided in this embodiment, Figure 3 It can be seen that this is the kidney wound left after kidney tumor resection. There is a capsule on the surface of the kidney, and the divided area is the wound. The kidney wound and the ruptured boundary of the renal capsule are marked in the picture.

[0074] like Figure 4 As shown, it is a kidney image before wound surface segmentation provided by an embodiment of the present invention. Figure 4 It can be seen that the state of the kidney before wound segmentation is displayed. During kidney wound segmentation, as the operation proceeds, the wound may be affected by factors such as the traction of surrounding tissues and the movement of surgical instruments, resulting in changes in the shape, size and position of the wound. At this time, the edge points and edge curves also change accordingly.

[0075] like Figure 5 As shown, it is a kidney image after wound surface segmentation provided by an embodiment of the present invention, which is composed of Figure 5 It can be seen that the state of the kidney after the wound surface is segmented is displayed.

[0076] like Figure 6 As shown, it is a surgical procedure splitting framework diagram provided by an embodiment of the present invention, which consists of Figure 6 As can be seen, the overall process of robot-assisted partial nephrectomy surgery is shown.

[0077] like Figure 7 As shown, it is a surgical stage diagram provided by an embodiment of the present invention, which is composed of Figure 7 It can be seen that the surgical stage and the corresponding annotation ID, entry and exit points, and marking points are displayed.

[0078] like Figure 8 As shown, it is a diagram of the surgical steps provided by an embodiment of the present invention, comprising Figure 8 It can be seen that the specific steps of the operation and the corresponding ID, operation stage, start status and end status are displayed.

[0079] To summarize, the embodiment of the present invention obtains the edge fuzzy index through the kidney image edge data and the preset reference image edge data, and then obtains the wound rule index according to the obtained kidney wound rule data and the preset reference wound rule data, and finally obtains the wound segmentation index by combining the obtained wound segmentation data and the preset reference segmentation data, thereby achieving a comprehensive improvement in the acquisition of kidney wound segmentation data, and then achieving an improvement in the accuracy of kidney wound segmentation, effectively solving the problem of low correlation between kidney wound segmentation and wound edge in the prior art.

[0080] There are a few points to note:

[0081] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0082] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0083] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0084] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A kidney wound intelligent segmentation system, characterized in that: It includes image enhancement module, edge fuzzy index acquisition module, wound surface rule index acquisition module and wound surface segmentation index acquisition module: The image enhancement module is used to obtain a renal wound surface image through a preset medical imaging device, and perform image enhancement processing on the renal wound surface image to obtain a characteristic wound surface image and renal image edge data; The edge blur index acquisition module is used to obtain the edge blur index according to the acquired kidney image edge data and the preset reference image edge data obtained from the preset database, and determine whether to perform image adjustment in combination with the preset edge threshold obtained from the preset database. If the image adjustment is not performed, the wound surface rule index acquisition module function is executed. The edge blur index is used to evaluate the edge blur degree of the characteristic wound surface image; The wound surface rule index acquisition module is used to acquire the wound surface rule index according to the acquired kidney wound surface rule data and the preset reference wound surface rule data acquired from the preset database, and determine whether to execute the wound surface segmentation index acquisition module function in combination with the preset wound surface rule threshold value acquired from the database, and the wound surface rule index is used to evaluate the edge rule situation of the characteristic wound surface image; The wound surface segmentation index acquisition module is used to perform segmentation processing on the characteristic wound surface image to obtain a segmented wound surface image and wound surface segmentation data, and to obtain a wound surface segmentation index in combination with preset reference segmentation data obtained from a preset database, and to perform segmentation adjustment based on the wound surface segmentation index and a preset segmentation threshold obtained from the preset database. The wound surface segmentation index is used to evaluate the segmentation status of the characteristic wound surface image.

2. The intelligent renal wound segmentation system according to claim 1, characterized in that: The specific process of obtaining the characteristic wound surface image is as follows: Scanning the kidney area by using a preset medical imaging device to obtain a kidney wound surface image, wherein the preset medical imaging device includes a computer tomography, a magnetic resonance imaging, and an ultrasound imaging device; Performing image enhancement processing on the kidney wound surface image to obtain a characteristic wound surface image, wherein the image enhancement processing includes contrast enhancement, edge enhancement and region enhancement.

3. The intelligent renal wound segmentation system according to claim 1, characterized in that: The specific process of obtaining the edge blur index based on the acquired kidney image edge data and the preset reference image edge data obtained from the preset database is as follows: Extracting edge data of the characteristic wound surface image to obtain kidney image edge data, wherein the kidney image edge data includes a first gray value, a second gray value, and a pixel point distance, and the kidney image edge data is used to describe the edge contour of the kidney wound surface in the characteristic wound surface image; Obtaining a preset gradient threshold and a preset gray value change rate threshold from a preset database, and obtaining an edge gradient amplitude deviation and a gray value change rate deviation in combination with kidney image edge data; A preset gradient deviation threshold and a preset gray value change rate deviation threshold are obtained from a preset database, and an edge blur index is obtained by combining the edge gradient amplitude deviation and the gray value change rate deviation.

4. The intelligent renal wound segmentation system according to claim 3, characterized in that: The edge blur index is calculated using the following formula: In the formula, α i represents the edge blur index of the i-th pixel on the characteristic wound surface image, i=1,2,...,n-1, i represents the number of the pixel on the characteristic wound surface image, n represents the total number of pixels on the characteristic wound surface image, R i G represents the gray value change rate deviation of the i-th pixel on the characteristic wound image. i Indicates the edge gradient amplitude deviation of the i-th pixel on the characteristic wound surface image, I i Represents the first gray value of the i-th pixel on the characteristic wound surface image, I i‘ Indicates the second gray value of the i-th pixel on the characteristic wound image, ΔX i represents the pixel distance corresponding to the i-th pixel on the characteristic wound image, G0 represents the preset gradient threshold, R0 represents the preset gray value change rate threshold, ΔR represents the preset gray value change rate deviation threshold, ΔG represents the preset gradient deviation threshold, It represents the first-order derivative of the gray value corresponding to the i-th pixel in the x-axis direction. It represents the first-order derivative of the gray value corresponding to the i-th pixel in the y-axis direction, and e represents a natural constant.

5. The intelligent renal wound segmentation system according to claim 1, characterized in that: The specific process of determining whether to perform image adjustment in combination with the preset edge threshold obtained from the preset database is as follows: Q1, judging whether the edge blur index is not higher than the preset edge threshold, if the edge blur index is not higher than the preset edge threshold, no image adjustment is performed, otherwise Q2 is executed; Q2, sharpen the characteristic wound surface image. When the monitored edge blur index is not higher than the preset edge threshold, the image adjustment ends. Otherwise, Q3 is executed. Q3, perform noise removal on the characteristic wound surface image, and end image adjustment when the monitored edge blur index is not higher than the preset edge threshold. Otherwise, the system prompts the preset personnel to reacquire the kidney wound surface image.

6. The intelligent renal wound segmentation system according to claim 1, characterized in that: The specific process of obtaining the kidney wound surface rule data is as follows: The kidney wound surface regular data are obtained by using a preset image measurement device, wherein the kidney wound surface regular data include tangent angle, curve arc length, and edge point distance.

7. The intelligent renal wound segmentation system according to claim 6, characterized in that: The specific process of obtaining the wound surface rule index is as follows: Obtaining a preset curvature deviation threshold from a preset database, and obtaining a curvature influence by combining the preset curvature threshold, kidney wound surface rule data, and the preset curvature deviation threshold; Obtaining a preset distance standard deviation threshold from a preset database, and obtaining a distance standard deviation influence by combining the preset distance standard deviation threshold, kidney wound rule data, and the preset distance standard deviation threshold; The preset weights are obtained from the preset database, and the wound surface regularity index is obtained by combining the obtained curvature influence and distance standard deviation influence. The preset weights include a preset regularity first weight and a preset regularity second weight.

8. The intelligent renal wound segmentation system according to claim 1, characterized in that: The specific process of segmenting the characteristic wound surface image to obtain the segmented wound surface image and wound surface segmentation data is as follows: The wound segmentation data is obtained through a three-dimensional slicer, and the wound segmentation data includes an intersection area, a union area, a number of positive pixels, and a number of labeled positive pixels. The intersection area represents the area of ​​the overlapping part of the wound area in the segmented wound image and the manually annotated wound area, and the union area represents the area of ​​the covered part after the wound area in the segmented wound image and the manually annotated wound area are combined.

9. The intelligent renal wound segmentation system according to claim 8, characterized in that: The specific process of obtaining the wound segmentation index is as follows: Obtain a preset intersection-and-union ratio threshold from a preset database, and obtain the preset intersection-and-union ratio threshold and the difference between the ratio of the intersection area and the union area by combining the intersection area and the union area; Obtain a preset segmentation accuracy threshold from a preset database, and obtain the difference between the preset segmentation accuracy threshold and the ratio of the number of positive pixels to the number of labeled positive pixels by combining the number of positive pixels and the number of labeled positive pixels; Obtaining a first segmentation weight and a preset second segmentation weight from a preset database, and combining a preset intersection-to-union ratio threshold and a difference between the ratio of the intersection area and the union area, a preset segmentation accuracy threshold and a difference between the ratio of the number of positive pixels and the number of labeled positive pixels to obtain a wound segmentation index; The preset reference segmentation data includes a preset segmentation accuracy threshold, a preset intersection-over-union threshold, a preset first segmentation weight and a preset second segmentation weight, and is combined with the wound surface segmentation data to obtain a wound surface segmentation index.

10. The intelligent renal wound segmentation system according to claim 1, characterized in that: The specific process of performing segmentation adjustment based on the wound surface segmentation index and the preset segmentation threshold obtained from the preset database is as follows: M1, judging whether the wound segmentation index is not lower than the preset segmentation threshold. If the wound segmentation index is not lower than the preset segmentation threshold, no segmentation adjustment is performed, otherwise, M2 is continued; M2, obtaining a preset segmentation threshold from a preset database, sending a prompt to the preset personnel to gradually reduce the segmentation area by a preset multiple of the preset segmentation threshold, and ending the segmentation adjustment when the monitored wound segmentation index is not lower than the preset segmentation threshold, otherwise continue to execute M3; M3, performs regional merging. When the monitored wound segmentation index is not lower than the preset segmentation threshold, the segmentation adjustment is terminated. Otherwise, feedback is given to the preset personnel to reacquire the kidney wound image.

Images