A method and system for synergistic segmentation of a vaginal canal

Abstract

The application discloses a kind of synergistic segmentation method and system for vaginal tube, it is related to medical image processing technical field, method includes: based on multiple vaginal tube target image output multiple vaginal tube area comprehensive characteristics;According to the lesion area segmentation of the target vaginal tube three-dimensional form model constructed according to each comprehensive characteristic generates multiple initial lesion area images and corresponding initial lesion segmentation boundary curve;Determine initial adaptive topological constraint by each initial lesion area image, and determine optimization adaptive topological constraint based on initial adaptive topological constraint;According to optimization adaptive topological constraint and self-adapting grid refinement strategy, determine prior curve and multiple prior segmentation curve;Determine multiple target lesion area images based on prior curve and the prior curvature distribution of each prior segmentation curve and optimization adaptive topological constraint;Each target lesion area image is converted into multiple vaginal tube lesion visual image.Based on the above scheme, improve the reliability of vaginal tube segmentation.

Description

Technical Field

[0001] The present invention relates to the technical field of medical image processing, and in particular to a collaborative segmentation method and system for vaginal canal. Background Art

[0002] In modern medical examinations, image segmentation technology has shown great potential in lesion segmentation of medical images obtained by electronic computed tomography, magnetic resonance imaging, etc.

[0003] The vaginal canal is a complex three-dimensional tubular structure whose morphology changes with physiological conditions, such as vaginal laxity and vaginismus. These changes can alter the local morphology and topology of the vaginal canal, posing challenges for lesion segmentation. Traditional vaginal canal segmentation methods typically rely on fixed topological models, which are difficult to adapt to the dynamic changes of the vaginal canal and result in low segmentation reliability. Summary of the Invention

[0004] The present invention provides a collaborative segmentation method and system for the vaginal canal, which solves the technical problem that traditional vaginal canal segmentation methods usually rely on a fixed topological model and are difficult to adapt to the dynamic changes of the vaginal canal, resulting in low reliability of vaginal canal segmentation.

[0005] The present invention provides a collaborative segmentation method for the vaginal canal, comprising:

[0006] Acquire multiple vaginal canal medical images according to a preset step length and perform preprocessing to determine multiple vaginal canal target images;

[0007] Performing multi-channel feature extraction and cross-dimensional feature interaction based on each of the vaginal canal target images to output comprehensive features of multiple vaginal canal regions;

[0008] Performing parametric modeling of the vaginal canal based on the comprehensive features to generate a target three-dimensional morphological model of the vaginal canal, performing lesion region segmentation on the target three-dimensional morphological model of the vaginal canal, and generating a plurality of initial lesion region images and corresponding initial lesion segmentation boundary curves;

[0009] Determining initial adaptive topological constraints based on each of the initial lesion region images, performing multi-directional collaborative analysis on each of the initial lesion region images under the constraints of the initial adaptive topological constraints, and determining optimized adaptive topological constraints;

[0010] Constructing a priori curves according to each of the initial lesion segmentation boundary curves under the constraint of the optimized adaptive topological constraint, and determining a plurality of priori segmentation curves according to the prior curves through an adaptive grid refinement strategy;

[0011] Performing image segmentation on the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve, and performing multi-directional collaborative analysis under the constraint of the optimized adaptive topological constraint to determine multiple target lesion area images;

[0012] The target lesion area images are subjected to image enhancement rendering and converted into a plurality of vaginal canal lesion visual images.

[0013] Optionally, the multi-channel feature extraction and cross-dimensional feature interaction are performed based on each of the vaginal canal target images to output comprehensive features of multiple vaginal canal regions, including:

[0014] Performing multi-channel feature extraction on each of the vaginal canal target images to obtain multiple channel features of each of the vaginal canal target images;

[0015] Inputting the associated channel features of each vaginal canal target image into the cross-dimensional interactive model in sequence for feature processing to obtain multiple vaginal canal change-sensitive area images of each vaginal canal target image;

[0016] Using a cluster analysis algorithm to classify the images of the vaginal canal change-sensitive regions to determine multiple vaginal canal regions;

[0017] performing edge detection on the vaginal canal change sensitive region image of each of the vaginal canal regions according to an edge detection algorithm to obtain multiple initial contours of each of the vaginal canal regions;

[0018] splicing the initial contours of the vaginal canal regions to obtain a sequence of initial contours of the vaginal canal regions;

[0019] The time series features of each initial contour sequence are extracted using a preset first time series model, and the dynamic change features of each vaginal canal area are output;

[0020] Using a preset second time series model to respectively detect data difference values ​​between adjacent initial contours in each initial contour sequence, and correlating relatively later initial contours with data difference values ​​greater than or equal to a preset tolerance value to form shape features of each of the vaginal canal regions;

[0021] The shape features and dynamic change features of each vaginal canal area are converted into the same dimension and then spliced ​​to output the comprehensive features of each vaginal canal area.

[0022] Optionally, performing parametric modeling of the vaginal canal according to each of the comprehensive features to generate a target vaginal canal three-dimensional morphological model, performing lesion region segmentation on the target vaginal canal three-dimensional morphological model, and generating a plurality of initial lesion region images and corresponding initial lesion segmentation boundary curves, includes:

[0023] The comprehensive features are fused by a Poisson fusion algorithm to obtain a four-dimensional feature array;

[0024] Treating the elements of the four-dimensional feature array as nodes in a minimum spanning tree, performing parameterization processing on the four-dimensional feature array using a minimum tree generation algorithm, and outputting the geometric shape and topological structure of the vaginal canal;

[0025] Performing curve fitting on each of the comprehensive features to obtain multiple smooth curves;

[0026] Using the smooth curves, the geometric shapes, and the topological structures, an initial three-dimensional morphological model of the vaginal canal is constructed based on surface reconstruction technology;

[0027] Performing lesion area feature enhancement on the initial vaginal canal three-dimensional morphological model through self-calibration convolution to obtain a target vaginal canal three-dimensional morphological model;

[0028] A U-net network is used to segment the target vaginal canal three-dimensional morphological model into lesion regions, and a plurality of initial lesion region images and corresponding initial lesion region segmentation boundary curves are determined.

[0029] Optionally, determining an initial adaptive topological constraint based on each of the initial lesion region images, performing multi-directional collaborative analysis on each of the initial lesion region images under the constraints of the initial adaptive topological constraint, and determining an optimized adaptive topological constraint, includes:

[0030] Performing image segmentation on each of the initial lesion region images using a minimum graph cut algorithm to obtain a plurality of initial segmented lesion region images and a plurality of initial cut sets;

[0031] Performing image feature analysis on each of the initial segmented lesion region images and each of the initial cut sets to determine initial adaptive topological constraints;

[0032] Under the constraints of the initial adaptive topological constraints, feature extraction in different directions is performed on each of the initial lesion region images, and multiple direction analysis results of each of the initial lesion region images are output;

[0033] If the directional analysis results in any same direction all meet the preset consistency difference range, the initial adaptive topological constraint is used as a regularization term to construct an initial energy functional;

[0034] Determining the extreme value condition of the initial energy functional by using a variational method, and iteratively solving the initial energy functional according to the extreme value condition by using a gradient descent method, and outputting an optimized cut set;

[0035] Performing image segmentation on the target vaginal canal three-dimensional morphological model using a minimum graph cut algorithm according to the optimized cut set to obtain multiple intermediate lesion area images;

[0036] Calculating an intersection-over-union ratio using each of the initial lesion region images and the corresponding intermediate lesion region images;

[0037] If any of the intersection-and-union ratios is greater than the intersection-and-union ratio threshold, the initial adaptive topology constraint is used as the optimized adaptive topology constraint;

[0038] If the directional analysis results in any of the same directions do not meet the preset consistency difference range or any of the intersection-and-union ratios is less than or equal to the intersection-and-union ratio threshold, the corresponding directional analysis results of each of the initial lesion area images are respectively input into the cross-dimensional interaction model for feature processing, and multiple optimized fusion directional analysis results of each of the initial lesion area images are output;

[0039] The optimized fusion direction analysis results are concatenated to generate an optimized multi-directional collaborative result, and the optimized multi-directional collaborative result is input into the attention model to output a direction weighted feature;

[0040] Weighting the directional weighted feature and the initial adaptive topology constraint to obtain a new initial adaptive topology constraint, and counting the number of updates in real time;

[0041] If the update times are equal to the preset times threshold, the initial adaptive topology constraint is used as the optimized adaptive topology constraint;

[0042] If the number of updates is less than the preset threshold, new multiple directional analysis results of each initial lesion area image are determined under the constraints of the initial adaptive topological constraints until any directional analysis results in the same direction meet the preset consistency difference range or the number of updates is equal to the preset threshold.

[0043] Optionally, constructing a priori curves according to each of the initial lesion segmentation boundary curves under the constraint of the optimized adaptive topological constraint, and determining multiple priori segmentation curves according to the prior curves through an adaptive grid refinement strategy, comprises:

[0044] Extracting a plurality of discrete points from each of the initial lesion segmentation boundary curves, and generating a priori curves based on each of the discrete points using a Bezier curve under the constraints of the optimized adaptive topological constraints;

[0045] Inputting the priori curve into a convolutional neural network and outputting the curve residual;

[0046] Using the curve residual as a regularization term to correct the prior curve to obtain a calibration curve;

[0047] filtering the calibration curve to output scale curves of multiple scales;

[0048] Calculating the curvature of each scale curve to determine the curvature distribution of each scale;

[0049] Extracting high curvature points and inflection points from each of the curvature distributions, and performing adaptive grid refinement on the regions where each of the high curvature points and each of the inflection points are located in the calibration curve to determine a refined curve;

[0050] Performing image segmentation on the refined curve using a minimum graph cut algorithm to determine a refined segmentation curve;

[0051] Using the priori curve and each of the refined segmentation curves to calculate errors, and using multiple errors to form an error distribution;

[0052] When the error distribution does not meet the preset error condition, adaptive grid refinement and control point density adjustment are performed in the refinement curve according to the error distribution, and a new refinement curve is determined until the error distribution meets the preset error condition, and the refined segmentation curve is determined as the prior segmentation curve.

[0053] Optionally, the image segmentation of the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve is performed, and multi-directional collaborative analysis is performed under the constraint of the optimized adaptive topological constraint to determine multiple target lesion area images, including:

[0054] Performing multi-scale curvature analysis on the prior curve and each prior segmentation curve to obtain a plurality of corresponding prior curvature distributions;

[0055] Fitting the priori curve and the priori curvature distribution of each priori segmentation curve respectively by a spline curve fitting technique to obtain a plurality of spline curves;

[0056] The curvature change rate of each spline curve is used for fitting to obtain a curvature change function, and the curvature change function is used as a regularization term to construct a curvature energy functional;

[0057] performing image segmentation on the target vaginal canal three-dimensional morphological model based on the curvature energy functional using a minimum graph cut algorithm to obtain multiple curvature lesion area images;

[0058] performing feature extraction in different directions on each of the curvature lesion region images under the constraint of optimized adaptive topological constraints, and outputting multiple curvature direction analysis results of each of the curvature lesion region images;

[0059] If the preset consistency difference range is satisfied between the analysis results of curvature direction in any same direction, the optimized adaptive topological constraint is used as a regularization term to construct the target energy functional;

[0060] If the directional analysis results in any of the same directions do not meet the preset consistency difference range, the curvature directional analysis results of each of the curvature lesion area images are respectively input into the cross-latitude interaction model for feature processing, and multiple curvature fusion directional analysis results of each of the curvature lesion area images are output and spliced ​​to generate a curvature multi-directional collaborative result;

[0061] The curvature direction weighted feature obtained by inputting the curvature multi-directional collaborative result into the attention model is weighted with the optimized adaptive topological constraint to obtain a new optimized adaptive topological constraint, and the number of optimization updates is counted in real time;

[0062] If the number of optimization updates is equal to a preset optimization number threshold, the new optimization adaptability topology constraint is used as a regularization term to construct a target energy functional;

[0063] If the number of optimization updates is less than the preset optimization number threshold, multiple new curvature direction analysis results of each of the curvature lesion area images are determined under the constraint of the optimization adaptive topological constraint until any curvature direction analysis results in the same direction meet the preset consistency difference range or the number of optimization updates is equal to the preset optimization number threshold;

[0064] Using a minimum graph cut algorithm to perform image segmentation on the target vaginal canal three-dimensional morphological model based on the target energy functional to obtain multiple optimized lesion area images;

[0065] If the standard deviation of the curvature distribution of any of the optimized lesion area images does not meet the convergence conditions, a new optimized adaptive topological constraint is determined based on the multi-directional collaborative results of the curvature of each of the curvature lesion area images until the standard deviation of the curvature distribution of any of the optimized lesion area images meets the preset convergence conditions or the number of optimization updates is equal to the preset optimization number threshold, and then the optimized lesion area image is determined as the target lesion area image.

[0066] Optionally, the feature processing process of the cross-dimensional interaction model includes:

[0067] Perform full permutation and combination of multiple input features of the associated input to obtain multiple feature combinations;

[0068] Dividing the input features in each feature combination into multiple weighted features and fusion features according to the sorting;

[0069] After weighted summing each weighted feature in each feature combination, channel splicing is performed with the corresponding fusion feature to obtain the corresponding splicing feature of each feature combination;

[0070] The key area features of each spliced ​​feature are extracted respectively through the cross-dimensional attention mechanism to obtain multiple output features.

[0071] Optionally, performing image enhancement rendering on each target lesion area image to convert the image into a plurality of vaginal canal lesion visual images includes:

[0072] Performing Gaussian smoothing filtering and morphological opening and closing operations on each of the target lesion area images to output a plurality of corrected lesion area images;

[0073] Calculating segmentation accuracy using each of the corrected lesion region images and the corresponding standard lesion region image;

[0074] If any of the segmentation accuracy does not meet the expected segmentation standard, each of the corrected lesion area images is used as a new target lesion area image until the segmentation accuracy meets the expected segmentation standard and is determined as the target vaginal canal segmentation lesion image;

[0075] Each target vaginal canal segmentation lesion image is converted into a vaginal canal lesion visual image through image rendering technology.

[0076] A second aspect of the present invention provides a collaborative segmentation system for a vaginal canal, comprising:

[0077] An image processing module is used to obtain multiple medical images of the vaginal canal according to a preset step length and perform preprocessing to determine multiple target images of the vaginal canal;

[0078] A feature extraction module, configured to perform multi-channel feature extraction and cross-dimensional feature interaction based on each of the vaginal canal target images, and output comprehensive features of multiple vaginal canal regions;

[0079] a preliminary segmentation module, configured to perform parametric modeling of the vaginal canal based on the comprehensive features to generate a target three-dimensional morphological model of the vaginal canal, perform lesion region segmentation on the target three-dimensional morphological model of the vaginal canal, and generate a plurality of initial lesion region images and corresponding initial lesion segmentation boundary curves;

[0080] A constraint optimization module is used to determine an initial adaptive topological constraint based on each of the initial lesion region images, perform multi-directional collaborative analysis on each of the initial lesion region images under the constraints of the initial adaptive topological constraint, and determine an optimized adaptive topological constraint;

[0081] a curve optimization module, configured to construct a priori curves according to each of the initial lesion segmentation boundary curves under the constraint of the optimized adaptive topological constraint, and determine a plurality of priori segmentation curves according to the prior curves through an adaptive grid refinement strategy;

[0082] a target segmentation module, configured to perform image segmentation on the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve, and to perform multi-directional collaborative analysis under the constraints of the optimized adaptive topological constraints to determine multiple target lesion area images;

[0083] The visualization module is used to perform image enhancement rendering on each target lesion area image and convert it into multiple vaginal canal lesion visual images.

[0084] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of any of the methods described above.

[0085] It can be seen from the above technical solutions that the present invention has the following advantages:

[0086] The above scheme of the present invention provides a collaborative segmentation method for the vaginal canal, comprising: obtaining a plurality of vaginal canal medical images of the vaginal canal according to a preset step size and performing preprocessing to determine a plurality of vaginal canal target images; performing multi-channel feature extraction and cross-dimensional feature interaction based on each vaginal canal target image, and outputting comprehensive features of a plurality of vaginal canal regions; performing parametric modeling of the vaginal canal according to each comprehensive feature to generate a target vaginal canal three-dimensional morphological model, performing lesion region segmentation on the target vaginal canal three-dimensional morphological model, and generating a plurality of initial lesion region images and corresponding initial lesion segmentation boundary curves; determining an initial adaptive topological constraint based on each initial lesion region image, and generating a plurality of initial lesion region images and corresponding initial lesion segmentation boundary curves; A multi-directional collaborative analysis is performed on the images of each initial lesion area under the constraint of the initial adaptive topological constraint to determine the optimized adaptive topological constraint; a priori curve is constructed according to the segmentation boundary curve of each initial lesion under the constraint of the optimized adaptive topological constraint, and multiple prior segmentation curves are determined based on the prior curve through an adaptive mesh refinement strategy; image segmentation is performed on the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve, and a multi-directional collaborative analysis is performed under the constraint of the optimized adaptive topological constraint to determine multiple target lesion area images; image enhancement rendering is performed on each target lesion area image and converted into multiple vaginal canal lesion visual images. Based on the above scheme, the medical images of the vaginal canal are preprocessed to ensure the clarity and integrity of the data. The three-dimensional morphological model of the target vaginal canal is reconstructed by capturing the target image features of the vaginal canal and parameterizing modeling. This is conducive to adapting to the dynamic change characteristics of the vaginal canal and accurately presenting the spatial structure and morphological characteristics of the vaginal canal. The collaborative segmentation mechanism of image segmentation, adaptive topological constraints, adaptive grid refinement strategy and curvature analysis is adopted to facilitate the combination of the dynamic change characteristics of the vaginal canal with topological constraints to achieve real-time response and adaptive adjustment to the changes of the vaginal canal, thereby improving the reliability of vaginal canal segmentation. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0088] Figure 1 A step flow chart of a collaborative segmentation method for a vaginal canal provided by the embodiment of the present application;

[0089] Figure 2 A feature processing schematic diagram of a cross-latitude interaction model provided by the embodiment of the present application;

[0090] Figure 3 A structural block diagram of a collaborative segmentation system for a vaginal canal provided by the embodiment of the present application. DETAILED DESCRIPTION

[0091] The embodiment of the present application provides a collaborative segmentation method and system for a vaginal canal, which is used to solve the technical problem that the conventional vaginal canal segmentation method usually depends on a fixed topological model, is difficult to adapt to the dynamic change characteristics of the vaginal canal, and results in low reliability of the vaginal canal segmentation.

[0092] In order to make the purposes, characteristics and advantages of the present application more obvious and easy to understand, the technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the following described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0093] Please refer to Figure 1 , Figure 1 A step flow chart of a collaborative segmentation method for a vaginal canal provided by the embodiment of the present application.

[0094] The present application provides a collaborative segmentation method for a vaginal canal, comprising:

[0095] Step 101, a plurality of vaginal canal medical images of the vaginal canal are obtained according to a preset step length and preprocessed to determine a plurality of vaginal canal target images.

[0096] Step 101 comprises the following sub-steps:

[0097] S11 adopts an image processing algorithm to perform preliminary denoising processing on each vaginal canal medical image to obtain a first medical image.

[0098] It should be noted that medical images of the vaginal canal can be obtained through high-resolution medical imaging equipment (such as MRI or CT). Assuming that we use a 3T MRI device and scan at a resolution of 5 mm, we can obtain multiple three-dimensional image data of the vaginal canal, which will show the condition of the vaginal canal from different angles and different time points; these data are usually interfered by noise, so it is necessary to use image processing algorithms for preliminary denoising, such as applying a denoising algorithm based on non-local means (Non-Local Means), which can smooth the image in the spatial domain while retaining edge details.

[0099] S12: If the image resolution of each first medical image is insufficient, a super-resolution reconstruction algorithm is used to reconstruct the image to obtain a second medical image.

[0100] It should be noted that if the image resolution after denoising is still insufficient, a super-resolution reconstruction algorithm can be used, such as the super-resolution reconstruction network (SRCNN) based on convolutional neural network (CNN), to perform super-resolution reconstruction of the image data to achieve a high resolution of 25 mm.

[0101] S13. Using an image registration algorithm to perform registration and calibration on each second medical image to obtain a third medical image.

[0102] It should be noted that for image data acquired at different time points or angles, feature point-based image registration technology can be used, such as using the SIFT (Scale-Invariant Feature Transform) algorithm for feature point extraction and matching, and using the RANSAC (Random Sample Consensus) algorithm for registration and calibration to obtain high-quality image data in a unified coordinate system.

[0103] S14. If each third medical image has image distortion or deformation, perform geometric correction using a geometric correction algorithm to obtain a fourth medical image.

[0104] It should be noted that if there is image distortion or deformation in the image data, the image data can be geometrically corrected using a geometric correction algorithm, such as applying a geometric correction algorithm based on a B-spline deformation model, to ensure that the morphological structure of the image data is accurate.

[0105] S15. Perform enhancement processing on each fourth medical image using an image enhancement algorithm to obtain a fifth medical image.

[0106] It should be noted that, based on the above data, the data is enhanced by an image enhancement algorithm, for example, a contrast enhancement algorithm based on histogram equalization is used to make the tissue structure clearer and the contrast higher.

[0107] S16, acquire blood perfusion images of the vaginal canal by dynamic contrast-enhanced MRI, and fuse each fifth medical image and the corresponding blood perfusion image by using a multi-modal image fusion algorithm to obtain a plurality of vaginal canal target images.

[0108] It should be noted that the image data is combined with the blood perfusion information inside the vaginal canal. The blood perfusion image can be obtained by using dynamic contrast-enhanced MRI (DCE-MRI) technology. The multi-modal image fusion technology is used to fuse the structural image and the blood perfusion information by using a mutual information-based fusion algorithm to obtain the vaginal canal target image containing the blood perfusion information. Thus, the high processing precision and the enhancement technology can be used comprehensively to obtain high-quality and detailed medical image data for clinical diagnosis and treatment.

[0109] Step 102, multi-channel feature extraction and cross-latitude feature interaction are performed based on each vaginal canal target image, and comprehensive features of a plurality of vaginal canal regions are output.

[0110] Step 102 includes the following sub-steps:

[0111] S21, multi-channel feature extraction is performed on each vaginal canal target image to obtain a plurality of channel features of each vaginal canal target image.

[0112] It should be noted that multi-channel feature extraction is performed on each vaginal canal target image to extract different dimension information such as R channel, G channel and B channel features.

[0113] S22, the associated channel features are sequentially input into a cross-latitude interaction model according to each vaginal canal target image for feature processing to obtain a plurality of vaginal canal change sensitive region images of each vaginal canal target image.

[0114] Preferably, the feature processing process of the cross-latitude interaction model includes:

[0115] The plurality of input features are fully arranged and combined to obtain a plurality of feature combinations;

[0116] The input features in each feature combination are respectively divided into a plurality of weighted features and fusion features according to the order;

[0117] Each weighted feature in each feature combination is weighted and summed, and then channel spliced with the corresponding fusion feature to obtain a spliced feature corresponding to each feature combination;

[0118] Each spliced feature is subjected to key region feature extraction by a cross-latitude attention mechanism to obtain a plurality of output features.

[0119] It should be noted that in the present embodiment, as Figure 2As shown, taking each vaginal canal target image (data) as a unit, the associated multiple channel features are used as the input features (X0, X1, ..., Xn) of the cross-dimensional interaction model for full permutation and combination. Taking the existence of three channel features (RGB) as an example, there are 3*2*1=6 combinations of full permutation, thus obtaining 6 feature combinations; in each feature combination, multiple input features (X0, X1, ..., Xn-1) are used as weighted features in sequence, and the input feature (Xn) is used as the fusion feature. The associated multiple weighted features are weighted summed, and the fusion feature, that is, the channel data without combining the hierarchical dimensions, is introduced as supplementary data for channel splicing, and the spliced ​​features are output. For example, the R and G channel features are weighted summed, and the B channel feature is used as supplementary data; then, each spliced ​​feature can be evaluated through the attention layer such as the cross-dimensional attention mechanism (Triplet Attention) or channel attention or spatial attention to identify the key areas, obtain the areas most sensitive to structural changes such as the vaginal canal wall thickness and lumen diameter, and output the image of the vaginal canal change-sensitive area.

[0120] S23. Using a cluster analysis algorithm to classify the images of the sensitive areas of the vaginal canal changes, and determining multiple vaginal canal areas.

[0121] It should be noted that after obtaining the images of the sensitive areas of the vaginal canal changes, they are divided into different categories using a cluster analysis algorithm, thereby determining multiple vaginal canal areas.

[0122] S24. Perform edge detection on the vaginal canal change-sensitive region image of each vaginal canal region according to an edge detection algorithm to obtain multiple initial contours of each vaginal canal region.

[0123] It should be noted that an edge detection algorithm such as the Canny edge detection algorithm is used to binarize the images of the sensitive areas of each vaginal canal change by setting thresholds such as 50 and 150, identify significant boundary changes in the area, and extract the contour to obtain multiple initial contours of each vaginal canal area.

[0124] S25. Splicing the initial contours of the vaginal canal regions to obtain an initial contour sequence of the vaginal canal regions.

[0125] It should be noted that multiple images contain n time points. In the image at each time point, the initial contours of multiple vaginal canal areas are detected. The initial contours of each vaginal canal area at n time points are spliced ​​together to obtain the corresponding initial contour sequence.

[0126] S26. Extracting time series features from each initial contour sequence using a preset first time series model, and outputting dynamic change features of each vaginal canal region.

[0127] It should be noted that the initial contour sequence is input into the first time series model, such as the LSTM model, for time series analysis. By obtaining data within the abnormal time point, the changes in each vaginal canal area, such as changes in vaginal canal wall thickness, changes in lumen diameter, and overall shape deformation, are tracked to extract the corresponding dynamic change features.

[0128] S27. Use the preset second time series model to respectively detect the data difference values ​​between adjacent initial contours in each initial contour sequence, and associate the relatively later initial contours with data difference values ​​greater than or equal to the preset tolerance value to form the shape features of each vaginal canal area.

[0129] It should be noted that the initial contours at different times in each initial contour sequence are integrated through a second time series model such as an LSTM model, and whether the data difference value between the nth initial contour and the n-1th initial contour is greater than or equal to a preset tolerance value (such as 1e-4) is determined to determine whether there is a significant difference in the data. When there is a significant difference, it is determined that there is difference data between the two initial contours that does not belong to the same area. The difference data part, i.e., the nth initial contour, is saved and finally uniformly constructed into the shape features of the vaginal canal area.

[0130] S28. Convert the shape features and dynamic change features of each vaginal canal region into the same dimension and then splice them together to output the comprehensive features of each vaginal canal region.

[0131] It should be noted that, through the integration of shape features and dynamic change features, comprehensive features reflecting the anatomical characteristics of each vaginal canal region can be formed.

[0132] Step 103: perform parametric modeling of the vaginal canal based on the comprehensive features to generate a target vaginal canal three-dimensional morphological model, perform lesion area segmentation on the target vaginal canal three-dimensional morphological model, and generate multiple initial lesion area images and corresponding initial lesion segmentation boundary curves.

[0133] Step 103 includes the following sub-steps:

[0134] S31. Fuse the comprehensive features through the Poisson fusion algorithm to obtain a four-dimensional feature array.

[0135] It should be noted that the Poisson fusion algorithm is used to fuse all comprehensive features to fill the gaps in feature representation and complete the data. In a manner similar to MRI slice splicing, a four-dimensional feature array (x, y, z, t) is output, where xyz represents the coordinate system and t represents time.

[0136] S32. Consider the elements of the four-dimensional feature array as nodes in a minimum spanning tree, use a minimum tree generation algorithm to perform parameterization processing on the four-dimensional feature array, and output the geometric shape and topological structure of the vaginal canal.

[0137] It should be noted that parameterizing the four-dimensional feature array can be understood as constructing a patch for every three elements in a certain order to obtain the geometric shape and topological structure corresponding to the vaginal canal. This order can be determined by treating the elements as nodes in a minimum spanning tree using a minimum spanning tree algorithm. The parameterization method can be used to describe the geometric shape of the vaginal canal as similar to a curved cylinder, whose parameters such as length and diameter can accurately represent the size of the vaginal canal. As for the topological structure, it can be described as a closed pipe with two openings.

[0138] S33. Perform curve fitting on each comprehensive feature to obtain multiple smooth curves.

[0139] It should be noted that, through curve fitting technology, the data points in the comprehensive features can be connected into a smooth curve.

[0140] S34. Using various smooth curves, geometric shapes and topological structures, an initial vaginal canal three-dimensional morphological model is constructed based on surface reconstruction technology.

[0141] It should be noted that based on various smooth curves, geometric shapes and topological structures, a three-dimensional morphological model similar to the real vaginal canal can be constructed through surface reconstruction technology. Please refer to the existing technology for details. In this initial three-dimensional morphological model of the vaginal canal, the subtle wrinkles of the vaginal canal wall and possible morphological variations can be clearly seen.

[0142] S35, performing lesion region feature enhancement on the initial vaginal canal 3D morphology model through self-calibration convolution to obtain a target vaginal canal 3D morphology model;

[0143] It should be noted that the initial vaginal canal 3D morphological model is input into the self-calibration convolution. When the data features of a certain area are detected to be different from those of the surrounding area, the self-calibration convolution will focus on this area to capture its local structural changes and detailed features. If the color, texture and other features of a certain area are found to be different from those of normal tissue, it may be a potential lesion area. The self-calibration convolution will identify and mark the feature points of this area.

[0144] In addition, in order to improve the accuracy and robustness of modeling, this embodiment can adopt an improved self-calibration convolution, that is, a self-calibration convolution that introduces batch normalization. By performing batch normalization after the convolution operation of the self-calibration convolution, it is ensured that the image features such as brightness and contrast in different regions remain consistent during the feature extraction process, reducing the impact of deviation and improving the convergence speed of the convolution layer when processing complex morphologies; at the same time, the three-dimensional morphological model of the vaginal canal can also dynamically adjust the learning rate and the parameters of the self-calibration convolution layer according to different data. For example, when the lesion features are not obvious, the learning rate and convolution layer parameters are automatically adjusted to better adapt to local features and more accurately capture the subtle differences in the lesion area.

[0145] S36. Use a U-net network to segment the target vaginal canal three-dimensional morphological model into lesion regions, and determine a plurality of initial lesion region images and corresponding initial lesion region segmentation boundary curves.

[0146] It should be noted that when the target vaginal canal three-dimensional morphological model is input into the U-net network, the U-net network will perform a preliminary segmentation of the lesion area based on the learned features, and obtain multiple initial lesion area images and corresponding initial lesion area segmentation boundary curves. The lesion area segmentation boundary curve can be understood as a curve that separates the lesion area from the normal tissue area and depicts the outline of the lesion.

[0147] Step 104 : determining initial adaptive topological constraints based on the initial lesion region images, performing multi-directional collaborative analysis on the initial lesion region images under the constraints of the initial adaptive topological constraints, and determining optimized adaptive topological constraints.

[0148] Step 104 includes the following sub-steps:

[0149] S41, performing image segmentation on each initial lesion region image using a minimum graph cut algorithm to obtain a plurality of initial segmented lesion region images and a plurality of initial cut sets;

[0150] It should be noted that based on the initial lesion area image, the minimum graph cut algorithm is applied to calculate the minimum cut. Assuming that the size of the input image is 100x100 pixels, the minimum graph cut algorithm first constructs a weight matrix by calculating the weights between pixels. These weights can be calculated by the pixel grayscale difference. For example, the weight between a pixel and its neighboring pixels can be set as w(i,j)=exp(|I(i)-I(j)| 2 / σ 2 ), where σ is the adjustment parameter; through the minimum cut operation, the initial segmented lesion area image and the initial cut set are obtained. The cut set can be understood as a set of edges that divide the pixels of the image into the foreground and background, mapping the potential topological structure in the image.

[0151] S42: Perform image feature analysis on each initially segmented lesion region image and each initial cut set to determine initial adaptive topological constraints.

[0152] It should be noted that, based on the initial segmented lesion area image and the initial cut set, texture and edge feature analysis of the image is performed to determine the initial adaptive topological constraints, such as the connectivity and smoothness of the segmentation curve.

[0153] S43 , performing feature extraction in different directions on each initial lesion region image under the constraint of the initial adaptive topological constraint, and outputting multiple direction analysis results of each initial lesion region image.

[0154] It should be noted that the multi-head convolutional neural network (MCNN) can extract features from the lesion area image from multiple different angles based on multiple parallel convolutional neural network layers, such as horizontal, vertical, and diagonal directions, or set the direction from 0 to 180 degrees in increments of 10 degrees. Its output is the directional analysis result after the feature extraction of each direction. These directional analysis results reflect information such as gradient changes in various directions;

[0155] When performing directional analysis, adaptive topological constraints can be used as a reference standard to ensure the rationality of the segmentation results. For example, adaptive topological constraints require that the segmentation curve be connected and smooth. When the initial lesion area image is directional analyzed from 0 degrees to 180 degrees in increments of 10 degrees, the adaptive topological constraint is like a "filter" to determine whether the results obtained in each direction meet the overall topological requirements. If the gradient change amplitude obtained in a certain direction appears reasonable locally, but causes the segmentation curve to be disconnected or not smooth, then it does not meet the adaptive topological constraint.

[0156] S44. If the directional analysis results in any same direction satisfy the preset consistency difference range, the initial adaptive topological constraint is used as a regularization term to construct the initial energy functional.

[0157] It should be noted that if the gradient change amplitudes between the analysis results in multiple directions in each direction are consistent within a certain range of difference, it is considered that the multi-directional synergy condition is met, and the initial adaptive topological constraint is integrated into the energy functional as a regularization term to construct the initial energy functional E(u)=Data(u)+λReg(u), where Data(u) represents the data term, Reg(u) represents the regularization term, λ is the weight parameter, and u represents the cut set.

[0158] S45. Use the variational method to determine the extreme conditions of the initial energy functional, and iteratively solve the initial energy functional according to the extreme conditions through the gradient descent method, and output the optimized cut set.

[0159] It should be noted that the initial energy functional is processed by the variational method, and δE / δu=0 is set to obtain the extreme value condition of the optimal solution. When the segmentation requirements are met, the gradient descent method is used to calculate the gradient ∇E(u) and update the curve parameters to output the optimized cut set.

[0160] S46. Perform image segmentation on the target vaginal canal three-dimensional morphological model using a minimum graph cut algorithm according to the optimized cut set to obtain multiple images of intermediate lesion areas.

[0161] S47. Calculate the intersection-over-union ratio using each initial lesion region image and the corresponding intermediate lesion region image.

[0162] S48. If any intersection-over-union ratio is greater than the intersection-over-union ratio threshold, the initial adaptive topology constraint is used as the optimized adaptive topology constraint.

[0163] It should be noted that the initial lesion area image and the intermediate lesion area image are compared, and indicators such as intersection over union (IoU) and the number of connected areas are used to determine whether the segmentation accuracy has improved. If the IoU increases from 6 to 8, it means that the segmentation accuracy has been significantly improved. Therefore, the initial adaptive topology constraint is used as the optimized adaptive topology constraint.

[0164] S49. If the directional analysis results in any same direction do not meet the preset consistency difference range or any intersection-and-union ratio is less than or equal to the intersection-and-union ratio threshold, the corresponding directional analysis results of each initial lesion area image are respectively input into the cross-dimensional interaction model for feature processing, and multiple optimized fusion directional analysis results of each initial lesion area image are output.

[0165] S410: Use the analysis results of each optimized fusion direction to generate an optimized multi-directional collaborative result, input the optimized multi-directional collaborative result into the attention model, and output the direction weighted feature.

[0166] S411 , weighting the direction weighted feature and the initial adaptive topology constraint to obtain a new initial adaptive topology constraint, and counting the number of updates in real time.

[0167] S412: If the number of updates is equal to the preset number threshold, the initial adaptive topology constraint is used as the optimized adaptive topology constraint.

[0168] It should be noted that, in this embodiment, the threshold of the number of iterative updates of the adaptive topology constraint can be determined according to the number of directions in the directionality analysis, for example, the number of directions is used as the threshold of the number of updates.

[0169] S413, if the number of updates is less than the preset number threshold, determine new multiple directional analysis results of each initial lesion region image under the constraint of the initial adaptive topology constraint until the directional analysis results of any same direction all satisfy the preset consistency difference range or the number of updates is equal to the preset number threshold.

[0170] It should be noted that if the directional analysis results in any direction do not satisfy the preset consistency difference range, it is considered that the multi-directional coordination condition is not satisfied, and the adaptive topology constraint needs to be modified and updated through the multi-specific direction iterative generation process; first, input the directional analysis results of each initial lesion region image into the cross-latitude interaction model for feature processing, and output multiple optimized fusion directional analysis results of each initial lesion region image; then, splice each optimized fusion directional analysis result to obtain a multi-directional coordination result, which can be understood as the weight of the initial lesion region image extracted by the multi-head convolutional neural network, and input the optimized multi-directional coordination result into the attention model to adjust the weight for refinement. The directional weighted features obtained are weighted with the initial adaptive topology constraint to update a new initial adaptive topology constraint, and the number of updates is counted in real time; if the number of updates is less than the preset number threshold, jump to S43 to re-analyze the directionality of the initial lesion region image under the constraint of the new initial adaptive topology constraint, if the multi-directional coordination condition is met, continue to jump to S44-S48 to output the optimized adaptive topology constraint, if the multi-directional coordination condition is still not met until the number of updates is equal to the preset number threshold, the current initial adaptive topology constraint is output as the optimized adaptive topology constraint.

[0171] If any of the intersection-over-union ratios calculated in S47 is greater than the intersection-over-union ratio threshold, the adaptive topology constraint needs to be modified and updated, and the initial adaptive topology constraint is updated according to steps S49-S13 to output the optimized adaptive topology constraint.

[0172] Step 105, constructing a prior curve according to each initial lesion segmentation boundary curve under the constraint of the optimized adaptive topology constraint, and determining multiple prior segmentation curves through an adaptive mesh refinement strategy according to the prior curve.

[0173] Step 105 includes the following sub-steps:

[0174] S51, extract multiple discrete points from each initial lesion segmentation boundary curve, and generate a prior curve based on each discrete point under the constraint of the optimized adaptive topology constraint using a Bezier curve.

[0175] It should be noted that for a group of initial lesion segmentation boundary curves composed of discrete points, an n-order (such as n=3) Bezier curve can be used to form a relatively smooth prior curve under the constraint of the optimized adaptive topology constraint.

[0176] S52, input the prior curve into the user convolutional neural network or the self-calibration convolutional neural network to output a curve residual.

[0177] It should be noted that the user convolutional neural network or the self-calibration convolutional neural network can predict the residual of the prior curve and output the curve residual.

[0178] S53, correct the prior curve by taking the curve residual as a regularization term to obtain a calibration curve.

[0179] It should be noted that the curve residual can be used as a regularization term to adjust the shape of the prior curve, thereby obtaining the calibrated calibration curve.

[0180] S54, filter the calibration curve to output a plurality of scale curves.

[0181] S55, calculate the curvature of each scale curve to determine the curvature distribution of each scale.

[0182] It should be noted that the curvature distribution of multiple scales is determined by performing multi-scale curvature analysis on the calibration curve; first, the calibration curve is filtered by using Gaussian smoothing, bilateral filtering, wavelet transform, etc. to obtain scale curves of different scales, and then the curvature is calculated by solving the second derivative of each point of the scale curve, and the curvature distribution is determined.

[0183] S56, extract high curvature points and inflection points from each curvature distribution, and perform adaptive grid refinement on the regions where the high curvature points and the inflection points are located in the calibration curve to determine a refined curve.

[0184] It should be noted that the multi-scale method can balance between details and global features, and the key points, i.e. high curvature points and inflection points, are identified from the curvature distribution of each scale. In the vicinity of these key points of the calibration curve, the original grid pieces are increased in refinement degree (for example, the grid density is increased by 20% in the high curvature area) to apply the adaptive grid refinement strategy, and the refined curve is obtained. In this way, the density of local control points can be improved, and the expression ability of the curve can be improved.

[0185] S57, perform image segmentation on the refined curve using a minimum cut algorithm to determine a refined segmentation curve.

[0186] It should be noted that the minimum cut algorithm is used to divide the refined curve into several parts based on the principle of energy minimization to obtain the refined segmentation curve, so as to realize accurate boundary segmentation.

[0187] S58, calculate errors between the prior curve and each refined segmentation curve, and construct an error distribution using a plurality of errors.

[0188] It should be noted that by refining the segmentation curve, the deviation between the refined segmentation curve and the prior curve shape can be determined. The error analysis can use the mean square error (MSE) to evaluate the difference between the segmentation result and the prior shape and obtain the error distribution.

[0189] S59. When the error distribution does not meet the preset error condition, adaptive mesh refinement and control point density adjustment are performed in the refinement curve according to the error distribution, and a new refinement curve is determined until the error distribution meets the preset error condition, and the refined segmentation curve is determined as the prior segmentation curve.

[0190] It should be noted that the error distribution can show the error conditions of different areas in the refinement curve, helping to determine which areas need to be adjusted and the degree of adjustment. Therefore, the adaptive grid refinement and control point density adjustment of the refinement curve according to the error distribution can be achieved through the error feedback mechanism. For example, additional control points are added or the grid density is adjusted in areas where the error is greater than a specific threshold; after the adjustment, jump to S57-S59 and use the minimum graph cut algorithm again for image segmentation and error analysis. When the error distribution meets the preset error condition, the current refined segmentation curve is output as a priori segmentation curve to determine the refined segmentation curve. Through this processing process, the representation of the curve shape and the segmentation accuracy can be effectively improved.

[0191] Step 106 : performing image segmentation on the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve, and performing multi-directional collaborative analysis under the constraint of optimized adaptive topological constraints to determine multiple target lesion area images.

[0192] Step 106 includes the following sub-steps:

[0193] S61. Perform multi-scale curvature analysis on the prior curve and each prior segmentation curve to obtain a plurality of corresponding prior curvature distributions.

[0194] It should be noted that multi-scale curvature analysis refers to identifying and quantifying the curvature changes of a curve at different scales, including filtering the curve and calculating the curvature to construct a curvature distribution. For details, please refer to S54-S55. Multi-scale curvature analysis is used to determine the prior curve and multiple prior curvature distributions of each curve in each prior segmentation curve.

[0195] S62, fitting the priori curve and the priori curvature distribution of each priori segmentation curve respectively by using a spline curve fitting technique to obtain a plurality of spline curves;

[0196] It should be noted that for the multi-scale curvature analysis results, spline curve fitting technology can be used to obtain the polynomial of the prior curvature distribution in three-dimensional space, and the geometric characteristics of the prior curvature distribution at different scales can be captured through the polynomial, and the prior curve and the spline curve of each curve in each prior segmentation curve can be output.

[0197] S63, fitting the curvature change rate of each spline curve to obtain a curvature change function, and constructing the curvature change function as a regularization term to obtain a curvature energy functional;

[0198] It should be noted that the corresponding curvature change rate can be determined according to the curvature of each spline curve. The curvature change rate is fitted to obtain the curvature change function and integrated into the energy functional as a regularization term to obtain the curvature energy functional E = E_data + λE_curvature, where λ represents the regularization parameter used to balance the influence of the data term and the curvature term, E_curvature represents the curvature change function, and E_data represents the data term, which can optimize the fitting accuracy of the high curvature area.

[0199] S64. Perform image segmentation on the target vaginal canal three-dimensional morphological model based on the curvature energy functional using a minimum graph cut algorithm to obtain multiple curvature lesion area images.

[0200] It should be noted that by minimizing the curvature energy functional, the minimum graph cut algorithm is used to re-segment the target vaginal canal three-dimensional morphological model to obtain multiple curvature lesion area images.

[0201] S65. Under the constraints of the optimized adaptive topological constraints, feature extraction is performed on each curvature lesion region image in different directions, and multiple curvature direction analysis results of each curvature lesion region image are output.

[0202] S66. If the curvature direction analysis results in any same direction satisfy the preset consistency difference range, the optimized adaptive topological constraint is used as a regularization term to construct the target energy functional.

[0203] S67. If the directional analysis results in any same direction do not meet the preset consistency difference range, the curvature directional analysis results of each curvature lesion area image are respectively input into the cross-dimensional interaction model for feature processing, and multiple curvature fusion directional analysis results of each curvature lesion area image are output and spliced ​​to generate curvature multi-directional collaborative results.

[0204] It should be noted that if any directional analysis results in the same direction do not meet the preset consistency difference range, then the image of each curvature lesion area is used as the unit, and the associated multiple curvature directional analysis results are used as the input features of the cross-dimensional interaction model. According to the feature processing process of the cross-dimensional interaction model, the permutation, combination, weighted splicing and feature extraction are performed to output the curvature fusion directional analysis results, and the various curvature fusion directional analysis results are spliced ​​to obtain the curvature multi-directional collaborative results.

[0205] S68. Input the curvature multi-directional collaborative results into the attention model to obtain the curvature direction weighted features, weight them with the optimized adaptive topological constraints, obtain new optimized adaptive topological constraints, and count the number of optimization updates in real time.

[0206] S69. If the number of optimization updates is equal to a preset optimization number threshold, the new optimization adaptive topology constraint is used as a regularization term to construct a target energy functional.

[0207] S610. If the number of optimization updates is less than the preset optimization threshold, multiple new curvature direction analysis results of each curvature lesion area image are determined under the constraints of the optimization adaptive topological constraints until the curvature direction analysis results of any same direction meet the preset consistency difference range or the number of optimization updates is equal to the preset optimization threshold.

[0208] S611. Use a minimum graph cut algorithm to perform image segmentation on the target vaginal canal three-dimensional morphological model based on the target energy functional to obtain multiple optimized lesion area images.

[0209] S612. If the standard deviation of the curvature distribution of any optimized lesion area image does not meet the convergence condition, a new optimized adaptive topological constraint is determined based on the multi-directional collaborative results of the curvature of each curvature lesion area image until the standard deviation of the curvature distribution of any optimized lesion area image meets the preset convergence condition or the number of optimization updates is equal to the preset optimization number threshold, and the optimized lesion area image is determined as the target lesion area image.

[0210] It should be noted that convergence can be determined by calculating whether the standard deviation of the curvature distribution of the optimized lesion area image is less than 1. If the convergence state reaches the preset standard, the iterative adaptive topological constraint is stopped, and the final vaginal canal segmentation result, i.e., the target lesion area image, is obtained; if it has not converged, when the number of optimization updates is less than the preset optimization number threshold, the iterative adaptive topological constraint continues. When the number of optimization updates is equal to the preset optimization number threshold, the latest optimized lesion area image is output as the target lesion area image.

[0211] Step 107: Perform image enhancement rendering on each target lesion region image to convert it into multiple vaginal canal lesion visual images.

[0212] Step 107 includes the following sub-steps:

[0213] S71 , performing Gaussian smoothing filtering and morphological opening and closing operations on each target lesion region image, and outputting a plurality of corrected lesion region images.

[0214] It should be noted that the target lesion area image is subjected to Gaussian smoothing filtering to obtain a preliminary smoothed lesion area image. In specific implementation, a Gaussian function with a standard deviation of 5 can be used for convolution operation. If the Gaussian smoothing filtering result shows that the noise is still large, the Gaussian function parameters are adjusted to improve the noise suppression effect and obtain a new smoothed lesion area image; the smoothed lesion area image is subjected to morphological opening and closing operations. In specific implementation, a structural element with a radius of 3 can be applied for morphological operations to improve the boundary and morphology of the segmentation result and obtain a corrected lesion area image.

[0215] S72. Calculate the segmentation accuracy using each corrected lesion region image and the corresponding standard lesion region image.

[0216] It should be noted that the accuracy of segmentation is evaluated by calculating the segmentation accuracy based on the corrected lesion area image by comparing it with the standard lesion area image as a benchmark. In specific implementation, the Dice coefficient or Jaccard index can be used for evaluation.

[0217] S73. If any segmentation accuracy does not meet the expected segmentation standard, each corrected lesion region image is used as a new target lesion region image until the segmentation accuracy meets the expected segmentation standard and is determined as the target vaginal canal segmentation lesion image.

[0218] It should be noted that if any segmentation accuracy does not meet the expected segmentation standard (such as 9), the corrected lesion area image is used as the new target lesion area image, and the process jumps to S71 to iteratively adjust the parameters of Gaussian filtering and morphological operations to obtain a more accurate corrected lesion area image.

[0219] In addition, by adding Gaussian noise and salt and pepper noise of different intensities to the target lesion area image, its adaptability to different noise and interference conditions can be evaluated to ensure that the collaborative segmentation method still maintains stable performance under the conditions of noise standard deviation of 5 and noise density of 0.2.

[0220] S74. Convert each target vaginal canal segmentation lesion image into a vaginal canal lesion visual image through image rendering technology.

[0221] It should be noted that image rendering techniques such as pseudo-color coding or three-dimensional reconstruction are used to convert the target vaginal canal segmentation lesion image into an intuitive visual image, which is convenient for subsequent image observation and analysis, provides a visual imaging reference for exploring the disease characteristics and pathological conditions related to the vaginal canal, and provides support for doctors to formulate appropriate treatment plans.

[0222] In an embodiment of the present invention, the medical images of the vaginal canal are preprocessed to ensure the clarity and integrity of the data. The three-dimensional morphological model of the target vaginal canal is reconstructed by capturing the target image features of the vaginal canal and parameterizing modeling, which is conducive to adapting to the dynamic change characteristics of the vaginal canal to accurately present the spatial structure and morphological characteristics of the vaginal canal. A collaborative segmentation mechanism of image segmentation, adaptive topological constraints, adaptive grid refinement strategy and curvature analysis is adopted to facilitate the combination of the dynamic change characteristics of the vaginal canal with topological constraints to achieve real-time response and adaptive adjustment to changes in the vaginal canal, improve the reliability of vaginal canal segmentation, and visualize the image of the target lesion area. The entire process is achieved through automated algorithms and image processing technology without the need for human intervention, thereby improving the efficiency of medical analysis and the accuracy of the results.

[0223] See also Figure 3 , Figure 3 This is a structural block diagram of a collaborative segmentation system for the vaginal canal provided by an embodiment of the present invention.

[0224] The present invention provides a collaborative segmentation system for vaginal canal, comprising:

[0225] The image processing module 301 is used to obtain multiple medical images of the vaginal canal according to a preset step size and perform preprocessing to determine multiple target images of the vaginal canal;

[0226] A feature extraction module 302 is configured to perform multi-channel feature extraction and cross-dimensional feature interaction based on each vaginal canal target image, and output comprehensive features of multiple vaginal canal regions;

[0227] The preliminary segmentation module 303 is used to perform parametric modeling of the vaginal canal based on the comprehensive features to generate a target three-dimensional morphological model of the vaginal canal, perform lesion region segmentation on the target three-dimensional morphological model of the vaginal canal, and generate multiple initial lesion region images and corresponding initial lesion segmentation boundary curves;

[0228] The constraint optimization module 304 is used to determine the initial adaptive topological constraints based on each initial lesion region image, perform multi-directional collaborative analysis on each initial lesion region image under the constraints of the initial adaptive topological constraints, and determine the optimized adaptive topological constraints;

[0229] The curve optimization module 305 is used to construct a priori curve according to each initial lesion segmentation boundary curve under the constraint of optimizing the adaptive topological constraint, and determine multiple priori segmentation curves according to the priori curve through an adaptive grid refinement strategy;

[0230] The target segmentation module 306 is used to perform image segmentation on the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve, and perform multi-directional collaborative analysis under the constraint of optimized adaptive topological constraints to determine multiple target lesion area images;

[0231] The visualization module 307 is used to perform image enhancement rendering on each target lesion area image and convert it into multiple vaginal canal lesion visual images.

[0232] Optionally, the feature extraction module 302 is specifically configured to:

[0233] Perform multi-channel feature extraction on each vaginal canal target image to obtain multiple channel features of each vaginal canal target image;

[0234] According to each vaginal canal target image, the associated channel features are sequentially input into the cross-dimensional interactive model for feature processing to obtain multiple vaginal canal change sensitive area images of each vaginal canal target image;

[0235] Cluster analysis algorithm is used to classify the images of each vaginal canal change-sensitive area to determine multiple vaginal canal areas;

[0236] Performing edge detection on the vaginal canal change sensitive region image of each vaginal canal region according to an edge detection algorithm to obtain multiple initial contours of each vaginal canal region;

[0237] splicing the initial contours of each vaginal canal region to obtain an initial contour sequence of each vaginal canal region;

[0238] The time series features of each initial contour sequence are extracted using a preset first time series model, and the dynamic change features of each vaginal canal area are output;

[0239] Using a preset second time series model to respectively detect data difference values ​​between adjacent initial contours in each initial contour sequence, the relatively later initial contours associated with data difference values ​​greater than or equal to a preset tolerance value constitute the shape features of each vaginal canal region;

[0240] The shape features and dynamic change features of each vaginal canal area are converted into the same dimension and then spliced ​​to output the comprehensive features of each vaginal canal area.

[0241] Optionally, the preliminary segmentation module 303 is specifically configured to:

[0242] The comprehensive features are fused through the Poisson fusion algorithm to obtain a four-dimensional feature array;

[0243] The elements of the four-dimensional feature array are regarded as nodes in a minimum spanning tree, and the minimum tree generation algorithm is used to parameterize the four-dimensional feature array to output the geometric shape and topological structure of the vaginal canal;

[0244] Perform curve fitting on each comprehensive feature to obtain multiple smooth curves;

[0245] Using various smooth curves, geometric shapes and topological structures, an initial three-dimensional morphological model of the vaginal canal was constructed based on surface reconstruction technology;

[0246] The lesion area features of the initial vaginal canal 3D morphology model are enhanced by self-calibration convolution to obtain the target vaginal canal 3D morphology model.

[0247] The U-net network is used to segment the lesion area of ​​the target vaginal canal three-dimensional morphological model, and multiple initial lesion area images and corresponding initial lesion area segmentation boundary curves are determined.

[0248] Optionally, the constraint optimization module 304 is specifically configured to:

[0249] Using a minimum graph cut algorithm to segment each initial lesion region image, a plurality of initial segmented lesion region images and a plurality of initial cut sets are obtained;

[0250] Perform image feature analysis on each initial segmented lesion area image and each initial cut set to determine the initial adaptive topological constraints;

[0251] Under the constraints of the initial adaptive topological constraints, feature extraction is performed on each initial lesion region image in different directions, and multiple direction analysis results of each initial lesion region image are output;

[0252] If the preset consistency difference range is satisfied between the directional analysis results in any same direction, the initial adaptive topological constraint is used as a regularization term to construct the initial energy functional;

[0253] The variational method is used to determine the extreme value conditions of the initial energy functional, and the gradient descent method is used to iteratively solve the initial energy functional according to the extreme value conditions, and the optimized cut set is output;

[0254] According to the optimized cut set, the minimum graph cut algorithm is used to segment the target vaginal canal 3D morphological model to obtain multiple images of the intermediate lesion area;

[0255] The intersection-over-union ratio is calculated using each initial lesion region image and the corresponding intermediate lesion region image;

[0256] If any intersection-and-union ratio is greater than the intersection-and-union ratio threshold, the initial adaptive topology constraint is used as the optimized adaptive topology constraint;

[0257] If the directional analysis results in any of the same directions do not meet the preset consistency difference range or any intersection-over-union ratio is less than or equal to the intersection-over-union ratio threshold, the corresponding directional analysis results of each initial lesion area image are input into the cross-dimensional interaction model for feature processing, and multiple optimized fusion directional analysis results of each initial lesion area image are output;

[0258] The optimized multi-directional collaborative results are generated by splicing the analysis results of each optimized fusion direction, and the optimized multi-directional collaborative results are input into the attention model to output direction weighted features;

[0259] The direction weighted features are weighted with the initial adaptive topology constraints to obtain new initial adaptive topology constraints, and the number of updates is counted in real time;

[0260] If the number of updates is equal to the preset number threshold, the initial adaptive topology constraint is used as the optimized adaptive topology constraint;

[0261] If the number of updates is less than the preset threshold, new multiple directional analysis results of each initial lesion area image are determined under the constraints of the initial adaptive topological constraints until any directional analysis results in the same direction meet the preset consistency difference range or the number of updates is equal to the preset threshold.

[0262] Optionally, the curve optimization module 305 is specifically configured to:

[0263] Extract multiple discrete points from each initial lesion segmentation boundary curve, and generate a priori curves based on each discrete point using Bezier curves under the constraints of optimizing adaptive topological constraints;

[0264] Input the prior curve into the convolutional neural network and output the curve residual;

[0265] The curve residual is used as a regularization term to modify the prior curve and obtain the calibration curve;

[0266] Filtering the calibration curve to output scale curves of multiple scales;

[0267] Calculate the curvature of each scale curve and determine the curvature distribution of each scale;

[0268] Extracting high curvature points and inflection points from each curvature distribution, performing adaptive mesh refinement on the regions where each high curvature point and each inflection point are located in the calibration curve, and determining a refinement curve;

[0269] The image is segmented using the minimum graph cut algorithm to determine the refined segmentation curve;

[0270] The error is calculated using the prior curve and each refined segmentation curve, and multiple errors are used to form the error distribution;

[0271] When the error distribution does not meet the preset error conditions, adaptive mesh refinement and control point density adjustment are performed in the refinement curve according to the error distribution, and a new refinement curve is determined until the error distribution meets the preset error conditions, and the refined segmentation curve is determined as the prior segmentation curve.

[0272] Optionally, the object segmentation module 306 is specifically configured to:

[0273] Perform multi-scale curvature analysis on the prior curve and each prior segmentation curve, and obtain multiple prior curvature distributions accordingly;

[0274] The priori curve and the priori curvature distribution of each priori segmentation curve are fitted respectively by spline curve fitting technology to obtain multiple spline curves;

[0275] The curvature variation function is obtained by fitting the curvature variation rate of each spline curve, and the curvature variation function is used as a regularization term to construct the curvature energy functional.

[0276] The target vaginal canal 3D morphological model is segmented using the minimum graph cut algorithm based on the curvature energy functional to obtain multiple curvature lesion area images.

[0277] Under the constraints of optimized adaptive topological constraints, feature extraction in different directions is performed on each curvature lesion region image, and multiple curvature direction analysis results of each curvature lesion region image are output;

[0278] If the preset consistency difference range is satisfied between the analysis results of curvature direction in any same direction, the optimized adaptive topological constraint is used as a regularization term to construct the target energy functional;

[0279] If the directional analysis results in any of the same directions do not meet the preset consistency difference range, the curvature directional analysis results of each curvature lesion area image are input into the cross-dimensional interaction model for feature processing, and multiple curvature fusion directional analysis results of each curvature lesion area image are output and spliced ​​to generate the curvature multi-directional collaborative result;

[0280] The curvature direction weighted features obtained by inputting the curvature multi-directional collaborative results into the attention model are weighted with the optimized adaptive topological constraints to obtain new optimized adaptive topological constraints, and the number of optimization updates is counted in real time.

[0281] If the number of optimization updates is equal to the preset optimization number threshold, the new optimization adaptability topology constraint is used as a regularization term to construct the target energy functional;

[0282] If the optimization update number is less than the preset optimization number threshold, new multiple curvature direction analysis results of each curvature lesion region image are determined under the constraint of the optimized adaptive topological constraint until the curvature direction analysis results of any same direction all satisfy the preset consistency difference range or the optimization update number is equal to the preset optimization number threshold.

[0283] The minimum graph cut algorithm is adopted to perform image segmentation on the target vaginal canal three-dimensional morphological model based on the target energy functional, and multiple optimized lesion region images are obtained.

[0284] If the standard deviation of the curvature distribution of any optimized lesion region image does not satisfy the convergence condition, a new optimized adaptive topological constraint is determined according to the curvature multi-direction collaborative result of each curvature lesion region image until the standard deviation of the curvature distribution of any optimized lesion region image satisfies the preset convergence condition or the optimization update number is equal to the preset optimization number threshold, and the optimized lesion region image is determined as the target lesion region image.

[0285] Optionally, the feature processing process of the cross-latitude interaction model includes:

[0286] The plurality of input features of the associated input are subjected to full permutation combination to obtain a plurality of feature combinations;

[0287] The input features in each feature combination are divided into a plurality of weighted features and fusion features according to the order;

[0288] The weighted sum of each weighted feature in each feature combination is performed, and the corresponding fusion feature is channel spliced to obtain a spliced feature corresponding to each feature combination;

[0289] The key region feature extraction is performed on each spliced feature through the cross-latitude attention mechanism to obtain a plurality of output features.

[0290] Optionally, the visualization module 307 is specifically configured to:

[0291] Gaussian smoothing filtering and morphological opening and closing operation are performed on each target lesion region image to output a plurality of corrected lesion region images;

[0292] The segmentation accuracy is calculated using each corrected lesion region image and the corresponding standard lesion region image;

[0293] If any segmentation accuracy does not satisfy the expected segmentation standard, each corrected lesion region image is used as a new target lesion region image until the segmentation accuracy satisfies the expected segmentation standard, and the target vaginal canal segmentation lesion image is determined;

[0294] Each target vaginal canal segmentation lesion image is converted into a vaginal canal lesion visual image through image rendering technology.

[0295] An embodiment of the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the collaborative segmentation method for the vaginal canal as described in any of the above embodiments.

[0296] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems and modules can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0297] In the several embodiments provided in this application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0298] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments can still be modified, or some of the technical features thereof can be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative segmentation method for the vaginal canal, characterized in that: include: Acquire multiple vaginal canal medical images according to a preset step length and perform preprocessing to determine multiple vaginal canal target images; Performing multi-channel feature extraction and cross-dimensional feature interaction based on each of the vaginal canal target images to output comprehensive features of multiple vaginal canal regions; Performing parametric modeling of the vaginal canal based on the comprehensive features to generate a target three-dimensional morphological model of the vaginal canal, performing lesion region segmentation on the target three-dimensional morphological model of the vaginal canal, and generating a plurality of initial lesion region images and corresponding initial lesion segmentation boundary curves; Determining initial adaptive topological constraints based on each of the initial lesion region images, performing multi-directional collaborative analysis on each of the initial lesion region images under the constraints of the initial adaptive topological constraints, and determining optimized adaptive topological constraints; Constructing a priori curves according to each of the initial lesion segmentation boundary curves under the constraint of the optimized adaptive topological constraint, and determining a plurality of priori segmentation curves according to the prior curves through an adaptive grid refinement strategy; Performing image segmentation on the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve, and performing multi-directional collaborative analysis under the constraint of the optimized adaptive topological constraint to determine multiple target lesion area images; The target lesion area images are subjected to image enhancement rendering and converted into a plurality of vaginal canal lesion visual images.

2. The collaborative segmentation method for the vaginal canal according to claim 1, characterized in that: The multi-channel feature extraction and cross-dimensional feature interaction based on each of the vaginal canal target images are performed to output comprehensive features of multiple vaginal canal regions, including: Performing multi-channel feature extraction on each of the vaginal canal target images to obtain multiple channel features of each of the vaginal canal target images; Inputting the associated channel features of each vaginal canal target image into the cross-dimensional interactive model in sequence for feature processing to obtain multiple vaginal canal change-sensitive area images of each vaginal canal target image; Using a cluster analysis algorithm to classify the images of the vaginal canal change-sensitive regions to determine multiple vaginal canal regions; performing edge detection on the vaginal canal change sensitive region image of each of the vaginal canal regions according to an edge detection algorithm to obtain multiple initial contours of each of the vaginal canal regions; splicing the initial contours of the vaginal canal regions to obtain a sequence of initial contours of the vaginal canal regions; The time series features of each initial contour sequence are extracted using a preset first time series model, and the dynamic change features of each vaginal canal area are output; Using a preset second time series model to respectively detect data difference values ​​between adjacent initial contours in each initial contour sequence, and correlating relatively later initial contours with data difference values ​​greater than or equal to a preset tolerance value to form shape features of each of the vaginal canal regions; The shape features and dynamic change features of each vaginal canal area are converted into the same dimension and then spliced ​​to output the comprehensive features of each vaginal canal area.

3. The collaborative segmentation method for the vaginal canal according to claim 1, characterized in that: The method of performing parametric modeling of the vaginal canal according to the comprehensive features to generate a target three-dimensional morphological model of the vaginal canal, performing lesion area segmentation on the target three-dimensional morphological model of the vaginal canal, and generating a plurality of initial lesion area images and corresponding initial lesion segmentation boundary curves includes: The comprehensive features are fused by a Poisson fusion algorithm to obtain a four-dimensional feature array; Treating the elements of the four-dimensional feature array as nodes in a minimum spanning tree, performing parameterization processing on the four-dimensional feature array using a minimum tree generation algorithm, and outputting the geometric shape and topological structure of the vaginal canal; Performing curve fitting on each of the comprehensive features to obtain multiple smooth curves; Using the smooth curves, the geometric shapes, and the topological structures, an initial three-dimensional morphological model of the vaginal canal is constructed based on surface reconstruction technology; Performing lesion area feature enhancement on the initial vaginal canal three-dimensional morphological model through self-calibration convolution to obtain a target vaginal canal three-dimensional morphological model; A U-net network is used to segment the target vaginal canal three-dimensional morphological model into lesion regions, and a plurality of initial lesion region images and corresponding initial lesion region segmentation boundary curves are determined.

4. The collaborative segmentation method for the vaginal canal according to claim 1, characterized in that: Determining the initial adaptive topological constraints based on the initial lesion region images, performing multi-directional collaborative analysis on the initial lesion region images under the constraints of the initial adaptive topological constraints, and determining the optimized adaptive topological constraints, includes: Performing image segmentation on each of the initial lesion region images using a minimum graph cut algorithm to obtain a plurality of initial segmented lesion region images and a plurality of initial cut sets; Performing image feature analysis on each of the initial segmented lesion region images and each of the initial cut sets to determine initial adaptive topological constraints; Under the constraints of the initial adaptive topological constraints, feature extraction in different directions is performed on each of the initial lesion region images, and multiple direction analysis results of each of the initial lesion region images are output; If the directional analysis results in any same direction all meet the preset consistency difference range, the initial adaptive topological constraint is used as a regularization term to construct an initial energy functional; Determining the extreme value condition of the initial energy functional by using a variational method, and iteratively solving the initial energy functional according to the extreme value condition by using a gradient descent method, and outputting an optimized cut set; Performing image segmentation on the target vaginal canal three-dimensional morphological model using a minimum graph cut algorithm according to the optimized cut set to obtain multiple intermediate lesion area images; Calculating an intersection-over-union ratio using each of the initial lesion region images and the corresponding intermediate lesion region images; If any of the intersection-and-union ratios is greater than the intersection-and-union ratio threshold, the initial adaptive topology constraint is used as the optimized adaptive topology constraint; If the directional analysis results in any of the same directions do not meet the preset consistency difference range or any of the intersection-and-union ratios is less than or equal to the intersection-and-union ratio threshold, the corresponding directional analysis results of each of the initial lesion area images are respectively input into the cross-dimensional interaction model for feature processing, and multiple optimized fusion directional analysis results of each of the initial lesion area images are output; The optimized fusion direction analysis results are concatenated to generate an optimized multi-directional collaborative result, and the optimized multi-directional collaborative result is input into the attention model to output a direction weighted feature; Weighting the directional weighted feature and the initial adaptive topology constraint to obtain a new initial adaptive topology constraint, and counting the number of updates in real time; If the update times are equal to the preset times threshold, the initial adaptive topology constraint is used as the optimized adaptive topology constraint; If the number of updates is less than the preset threshold, new multiple directional analysis results of each initial lesion area image are determined under the constraints of the initial adaptive topological constraints until any directional analysis results in the same direction meet the preset consistency difference range or the number of updates is equal to the preset threshold.

5. The collaborative segmentation method for the vaginal canal according to claim 1, characterized in that: The step of constructing a priori curves according to each of the initial lesion segmentation boundary curves under the constraint of the optimized adaptive topological constraint, and determining a plurality of priori segmentation curves according to the prior curves through an adaptive grid refinement strategy includes: Extracting a plurality of discrete points from each of the initial lesion segmentation boundary curves, and generating a priori curves based on each of the discrete points using a Bezier curve under the constraints of the optimized adaptive topological constraints; Inputting the priori curve into a convolutional neural network and outputting the curve residual; Using the curve residual as a regularization term to correct the prior curve to obtain a calibration curve; filtering the calibration curve to output scale curves of multiple scales; Calculating the curvature of each scale curve to determine the curvature distribution of each scale; Extracting high curvature points and inflection points from each of the curvature distributions, and performing adaptive grid refinement on the regions where each of the high curvature points and each of the inflection points are located in the calibration curve to determine a refined curve; Performing image segmentation on the refined curve using a minimum graph cut algorithm to determine a refined segmentation curve; Using the priori curve and each of the refined segmentation curves to calculate errors, and using multiple errors to form an error distribution; When the error distribution does not meet the preset error condition, adaptive grid refinement and control point density adjustment are performed in the refinement curve according to the error distribution, and a new refinement curve is determined until the error distribution meets the preset error condition, and the refined segmentation curve is determined as the prior segmentation curve.

6. The collaborative segmentation method for the vaginal canal according to claim 1, characterized in that: The method of performing image segmentation on the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve, and performing multi-directional collaborative analysis under the constraint of the optimized adaptive topological constraint to determine multiple target lesion area images includes: Performing multi-scale curvature analysis on the prior curve and each prior segmentation curve to obtain a plurality of corresponding prior curvature distributions; Fitting the priori curve and the priori curvature distribution of each priori segmentation curve respectively by a spline curve fitting technique to obtain a plurality of spline curves; The curvature change rate of each spline curve is used for fitting to obtain a curvature change function, and the curvature change function is used as a regularization term to construct a curvature energy functional; performing image segmentation on the target vaginal canal three-dimensional morphological model based on the curvature energy functional using a minimum graph cut algorithm to obtain multiple curvature lesion area images; performing feature extraction in different directions on each of the curvature lesion region images under the constraint of optimized adaptive topological constraints, and outputting multiple curvature direction analysis results of each of the curvature lesion region images; If the preset consistency difference range is satisfied between the analysis results of curvature direction in any same direction, the optimized adaptive topological constraint is used as a regularization term to construct the target energy functional; If the directional analysis results in any of the same directions do not meet the preset consistency difference range, the curvature directional analysis results of each of the curvature lesion area images are respectively input into the cross-latitude interaction model for feature processing, and multiple curvature fusion directional analysis results of each of the curvature lesion area images are output and spliced ​​to generate a curvature multi-directional collaborative result; The curvature direction weighted feature obtained by inputting the curvature multi-directional collaborative result into the attention model is weighted with the optimized adaptive topological constraint to obtain a new optimized adaptive topological constraint, and the number of optimization updates is counted in real time; If the number of optimization updates is equal to a preset optimization number threshold, the new optimization adaptability topology constraint is used as a regularization term to construct a target energy functional; If the number of optimization updates is less than the preset optimization number threshold, multiple new curvature direction analysis results of each of the curvature lesion area images are determined under the constraint of the optimization adaptive topological constraint until any curvature direction analysis results in the same direction meet the preset consistency difference range or the number of optimization updates is equal to the preset optimization number threshold; Using a minimum graph cut algorithm to perform image segmentation on the target vaginal canal three-dimensional morphological model based on the target energy functional to obtain multiple optimized lesion area images; If the standard deviation of the curvature distribution of any of the optimized lesion area images does not meet the convergence conditions, a new optimized adaptive topological constraint is determined based on the multi-directional collaborative results of the curvature of each of the curvature lesion area images until the standard deviation of the curvature distribution of any of the optimized lesion area images meets the preset convergence conditions or the number of optimization updates is equal to the preset optimization number threshold, and then the optimized lesion area image is determined as the target lesion area image.

7. The collaborative segmentation method for the vaginal canal according to claim 2, 4 or 6, characterized in that: The feature processing process of the cross-dimensional interaction model includes: Perform full permutation and combination of multiple input features of the associated input to obtain multiple feature combinations; Dividing the input features in each feature combination into multiple weighted features and fusion features according to the sorting; After weighted summing each weighted feature in each feature combination, channel splicing is performed with the corresponding fusion feature to obtain the corresponding splicing feature of each feature combination; The key area features of each spliced ​​feature are extracted respectively through the cross-dimensional attention mechanism to obtain multiple output features.

8. The collaborative segmentation method for the vaginal canal according to claim 1, characterized in that: The step of performing image enhancement rendering on each target lesion region image to convert the image into a plurality of vaginal canal lesion visual images includes: Performing Gaussian smoothing filtering and morphological opening and closing operations on each of the target lesion area images to output a plurality of corrected lesion area images; Calculating segmentation accuracy using each of the corrected lesion region images and the corresponding standard lesion region image; If any of the segmentation accuracy does not meet the expected segmentation standard, each of the corrected lesion area images is used as a new target lesion area image until the segmentation accuracy meets the expected segmentation standard and is determined as the target vaginal canal segmentation lesion image; Each target vaginal canal segmentation lesion image is converted into a vaginal canal lesion visual image through image rendering technology.

9. A collaborative segmentation system for the vaginal canal, characterized in that: include: An image processing module is used to obtain multiple medical images of the vaginal canal according to a preset step length and perform preprocessing to determine multiple target images of the vaginal canal; A feature extraction module, configured to perform multi-channel feature extraction and cross-dimensional feature interaction based on each of the vaginal canal target images, and output comprehensive features of multiple vaginal canal regions; a preliminary segmentation module, configured to perform parametric modeling of the vaginal canal based on the comprehensive features to generate a target three-dimensional morphological model of the vaginal canal, perform lesion region segmentation on the target three-dimensional morphological model of the vaginal canal, and generate a plurality of initial lesion region images and corresponding initial lesion segmentation boundary curves; A constraint optimization module is used to determine an initial adaptive topological constraint based on each of the initial lesion region images, perform multi-directional collaborative analysis on each of the initial lesion region images under the constraints of the initial adaptive topological constraint, and determine an optimized adaptive topological constraint; a curve optimization module, configured to construct a priori curves according to each of the initial lesion segmentation boundary curves under the constraint of the optimized adaptive topological constraint, and determine a plurality of priori segmentation curves according to the prior curves through an adaptive grid refinement strategy; a target segmentation module, configured to perform image segmentation on the target vaginal canal three-dimensional morphological model based on the prior curve and the prior curvature distribution of each prior segmentation curve, and to perform multi-directional collaborative analysis under the constraints of the optimized adaptive topological constraints to determine multiple target lesion area images; The visualization module is used to perform image enhancement rendering on each target lesion area image and convert it into multiple vaginal canal lesion visual images.

10. A computer device, characterized in that: The invention comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the collaborative segmentation method for the vaginal canal according to any one of claims 1 to 8.

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