A Finite Element Analysis Method and System for Visual Reference of Hysteroscopic Corners

Abstract

The present invention provides a finite element analysis method and system for visual reference of uterine cornua in hysteroscopy, which relates to the technical field of hysteroscopy. The method includes: acquiring internal uterine image data by using a hysteroscope, and performing grayscale processing on the color images in the internal uterine image data to obtain grayscale images; performing edge detection on the grayscale images to identify the edge contours of the uterus and uterine cornua, and extracting candidate positions of uterine cornu points according to the geometric features of the uterine cornua, including curvature changes, angle sharpness or specific positions on the edge contours; screening and verifying the candidate positions to determine the final positions of the uterine cornu points; taking the final positions of the uterine cornu points as a reference to establish a geometric coordinate system corresponding to the image coordinate system, and automatically identifying and determining the coordinates of two uterine cornu points inside the uterus. The present invention acquires uterine images through a hysteroscope, identifies uterine cornu points through processing and establishes a geometric model, and performs finite element analysis to accurately evaluate the structural characteristics of the uterus.

Description

Technical Field

[0001] The present invention relates to the technical field of hysteroscopy, and particularly to a finite element analysis method and system for the visual reference of uterine cornua in hysteroscopy. Background Art

[0002] In the traditional image processing method of hysteroscopy, some ignore the rich detailed information in the color image during grayscale processing. Therefore, the quality of the grayscale image is degraded, which in turn affects the accuracy of subsequent edge detection and the recognition of uterine cornu points.

[0003] Secondly, some of the existing edge detection technologies have limited ability to recognize the edge contours of the uterus and uterine cornua. Especially when facing complex internal uterine structures and image noise, it is difficult to accurately extract the candidate positions of uterine cornu points. This not only increases the difficulty of subsequent screening and verification work, but also may introduce errors and affect the determination of the final positions of uterine cornu points. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a finite element analysis method and system for the visual reference of uterine cornua in hysteroscopy, reducing the interference of human factors and improving the accuracy and efficiency of evaluation.

[0005] To solve the above technical problem, the technical solution of the present invention is as follows:

[0006] In the first aspect, a finite element analysis method for the visual reference of uterine cornua in hysteroscopy, the method includes:

[0007] Obtain the internal uterine image data by using a hysteroscope, and perform grayscale processing on the color image in the internal uterine image data to obtain a grayscale image;

[0008] Perform edge detection on the grayscale image, recognize the edge contours of the uterus and uterine cornua, and extract the candidate positions of uterine cornu points according to the geometric features of the uterine cornua, including curvature change, angle sharpness or specific positions on the edge contour;

[0009] Screen and verify the candidate positions to determine the final positions of uterine cornu points;

[0010] Taking the final positions of uterine cornu points as a reference, establish a geometric coordinate system corresponding to the image coordinate system, and automatically recognize and determine the coordinates of two uterine cornu points inside the uterus;

[0011] Taking the coordinates of uterine cornu points and the length of the crossbar as a reference, calculate the actual distance value between the two uterine cornu points;

[0012] According to the coordinates of uterine cornu points, the actual distance value between the two uterine cornu points and the internal uterine image data, construct a geometric model of the uterus and uterine cornu;

[0013] According to the geometric model of the uterus and uterine horns, perform finite element analysis to evaluate the stress distribution, strain state, and deformation degree of the uterine structure.

[0014] Furthermore, perform edge detection on the grayscale image to identify the edge contours of the uterus and uterine horns, and according to the geometric features of the uterine horns, including curvature changes, angle sharpness, or specific positions on the edge contour, extract the candidate positions of the uterine horn points, including:

[0015] Perform edge detection on the grayscale image to identify the edges of the uterus and uterine horns to obtain the edge detection result;

[0016] According to the edge detection result, extract the contours of the uterus and uterine horns;

[0017] Traverse the contours of the uterus and uterine horns, and according to the geometric features of the uterine horns, including curvature changes, angle sharpness, or specific positions on the contour, set judgment conditions to obtain the image marked with the candidate positions of the uterine horn points and the candidate position list;

[0018] Mark each point in the candidate position list again to obtain the final marked image, and extract the candidate positions of the uterine horn points from the final marked image.

[0019] Furthermore, perform edge detection on the grayscale image to identify the edges of the uterus and uterine horns to obtain the edge detection result, including:

[0020] Traverse each pixel in the grayscale image, calculate the change in the grayscale value of the pixels around each pixel, and generate the grayscale value change information of each pixel;

[0021] According to a preset threshold, judge whether each pixel is an edge point to obtain the edge point judgment result;

[0022] According to the edge point judgment result, generate a binary image, in which white pixels represent the detected edge points and black pixels represent non-edge regions, and use the binary image as the edge detection result.

[0023] Furthermore, the calculation formula for the actual distance value between two uterine horn points is:

[0024] The calculation formula for the actual distance value between two uterine horn points is:

[0025] ;

[0026] Wherein, represents the actual distance value between two uterine horn points; , represent the abscissas of the uterine horn points in the geometric coordinate system; , represent the ordinates of the uterine horn points in the geometric coordinate system; , respectively represent the scaling factors in the direction and the direction; represents the overall scaling factor; , respectively represent the offsets in the direction and the direction; , respectively represent the noise terms in the direction and the direction.

[0027] Furthermore, based on the cornu coordinates, the actual distance value between two cornua, and the uterine internal image data, a geometric model of the uterus and cornua is constructed, including:

[0028] Based on the cornu coordinates and the actual distance value between two cornua, the geometric shape of the uterus and cornua is constructed, including lines and arcs;

[0029] The geometric shapes of the uterus and cornua, including lines and arcs, are combined to construct an initial geometric contour of the uterus and cornua;

[0030] The shape and size of the initial geometric contour of the uterus and cornua are adjusted to obtain an adjusted geometric contour of the uterus and cornua;

[0031] Based on the adjusted geometric contour of the uterus and cornua, a geometric model of the uterus and cornua is constructed.

[0032] Furthermore, based on the geometric model of the uterus and cornua, finite element analysis is performed to evaluate the stress distribution, strain state, and deformation degree of the uterine structure, including:

[0033] Define material properties for the uterine tissue, including elastic modulus and Poisson's ratio parameters;

[0034] Perform mesh generation on the geometric model of the uterus and cornua, dividing the geometric model of the uterus and cornua into multiple small, interconnected elements;

[0035] Set boundary conditions and apply loads to the geometric model of the uterus and cornua, including simulating the pressure change in the uterus or external forces;

[0036] Perform finite element analysis on the geometric model of the uterus and cornua through a solver. During the analysis process, the solver calculates the stress, strain, and deformation of each element according to the set boundary conditions, loads, and the material properties of the uterine tissue to obtain finite element analysis result data, including stress distribution diagrams, strain state diagrams, and deformation degree diagrams;

[0037] Analyze the stress distribution diagram, strain state diagram, and deformation degree diagram to evaluate the stress distribution, strain state, and deformation degree of the uterine structure.

[0038] Furthermore, analyze the stress distribution diagram, strain state diagram, and deformation degree diagram to evaluate the stress distribution, strain state, and deformation degree of the uterine structure, including:

[0039] Analyze the stress distribution diagram, strain state diagram, and deformation degree diagram to identify high-stress areas and low-stress areas, stress gradient changes, and the magnitude and direction of strain and deformation, so as to obtain the analysis results of the stress distribution, strain state, and deformation degree diagrams;

[0040] Based on the analysis results of the stress distribution, strain state, and deformation degree diagrams, comprehensively evaluate the uterine structure to identify problem areas, including stress concentration, excessive strain, or abnormal deformation, so as to evaluate the stress distribution, strain state, and deformation degree of the uterine structure.

[0041] In a second aspect, a hysteroscopic uterine horn visual reference finite element analysis system includes:

[0042] An acquisition module for using a hysteroscope to acquire internal uterine image data, grayscale the color image in the internal uterine image data to obtain a grayscale image; perform edge detection on the grayscale image to identify the edge contours of the uterus and uterine horns, and extract candidate positions of uterine horn points according to the geometric features of the uterine horns, including curvature changes, angle sharpness, or specific positions on the edge contour; screen and verify the candidate positions to determine the final positions of the uterine horn points;

[0043] A processing module for establishing a geometric coordinate system corresponding to the image coordinate system based on the final positions of the uterine horn points, automatically identifying and determining the coordinates of two uterine horn points inside the uterus; calculating the actual distance value between the two uterine horn points based on the uterine horn point coordinates and the crossbar length; constructing a geometric model of the uterus and uterine horns according to the uterine horn point coordinates, the actual distance value between the two uterine horn points, and the internal uterine image data; performing finite element analysis according to the geometric model of the uterus and uterine horns to evaluate the stress distribution, strain state, and deformation degree of the uterine structure.

[0044] In a third aspect, a computing device includes:

[0045] One or more processors;

[0046] A storage device for storing one or more programs, which when executed by the one or more processors, cause the one or more processors to implement the described method.

[0047] Fourth aspect, a computer-readable storage medium stores a program which, when executed by a processor, implements the method described above.

[0048] The above solution of the present invention has at least the following beneficial effects:

[0049] By using a hysteroscope to obtain the internal image data of the uterus and performing image processing techniques such as grayscale processing and edge detection, the edge contours of the uterus and uterine horns can be identified more accurately, thereby accurately extracting the positions of the uterine horn points. This helps to understand the shape and structure of the uterus more accurately and improve the accuracy of diagnosis. It can automatically identify and determine the coordinates of the two uterine horn points inside the uterus and calculate the actual distance value between the two uterine horn points. This realizes automated measurement, reduces the interference of human factors, and improves the accuracy and efficiency of measurement. At the same time, based on these coordinates and distance values, a geometric model of the uterus and uterine horns can be automatically constructed, providing a basis for subsequent finite element analysis.

[0050] Through finite element analysis, the stress distribution, strain state, and deformation degree of the uterine structure can be comprehensively evaluated. This helps to understand the mechanical properties of the uterus under different physiological states more deeply. The accurate geometric model of the uterus and uterine horns and the results of finite element analysis can provide important references for surgical planning and navigation. Doctors can formulate more precise surgical plans based on these results, reduce surgical risks, and improve the success rate of surgery. By processing and analyzing a large amount of uterine image data, the variation law of the uterine shape can be revealed, providing a new perspective and method for uterine-related research. Description of the Drawings

[0051] Figure 1 is a schematic flowchart of a method for finite element analysis of the visual reference of uterine horns by hysteroscope provided by an embodiment of the present invention.

[0052] Figure 2 is a schematic diagram of a system for finite element analysis of the visual reference of uterine horns by hysteroscope provided by an embodiment of the present invention. Detailed Embodiments

[0053] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0054] As Figure 1 shown, an embodiment of the present invention proposes a method for finite element analysis of the visual reference of uterine horns by hysteroscope, and the method includes the following steps:

[0055] Step 11: Obtain the internal uterine image data using a hysteroscope, and perform grayscale processing on the color images in the internal uterine image data to obtain grayscale images;

[0056] Step 12: Perform edge detection on the grayscale images to identify the edge contours of the uterus and the uterine horns, and extract the candidate positions of the uterine horn points according to the geometric features of the uterine horns, including curvature changes, angle sharpness, or specific positions on the edge contours;

[0057] Step 13: Screen and verify the candidate positions to determine the final positions of the uterine horn points;

[0058] Step 14: Establish a geometric coordinate system corresponding to the image coordinate system based on the final positions of the uterine horn points, and automatically identify and determine the coordinates of the two uterine horn points inside the uterus;

[0059] Step 15: Calculate the actual distance value between the two uterine horn points based on the uterine horn point coordinates and the crossbar length as a reference;

[0060] Step 16: Construct a geometric model of the uterus and uterine horns based on the uterine horn point coordinates, the actual distance value between the two uterine horn points, and the internal uterine image data;

[0061] Step 17: Perform finite element analysis based on the geometric model of the uterus and uterine horns to evaluate the stress distribution, strain state, and deformation degree of the uterine structure.

[0062] In the embodiment of the present invention, the hysteroscope technology can directly observe the internal situation of the uterus and obtain high-quality image data. The grayscale processing simplifies the image information and highlights the edges and contours. The edge detection can accurately identify the edge contours of the uterus and the uterine horns, providing a basis for the extraction of uterine horn points and the construction of geometric models. By extracting the candidate positions of the uterine horn points and performing screening and verification, it can ensure that the finally determined positions of the uterine horn points are accurate and reliable. This helps to improve the accuracy of subsequent geometric model construction and the accuracy of finite element analysis. Based on the final positions of the uterine horn points, establishing a geometric coordinate system corresponding to the image coordinate system can achieve the automatic identification and determination of the coordinates of the two uterine horn points inside the uterus. By calculating the actual distance value between the two uterine horn points, important dimensional parameters can be provided for geometric model construction and finite element analysis. This helps to more comprehensively understand the structure and size of the uterus and provide more accurate information for the diagnosis and treatment of diseases.

[0063] Based on the cornu coordinates, the actual distance value between two cornu points, and the uterine internal image data, a geometric model of the uterus and its cornua can be constructed. This helps doctors more intuitively understand the three-dimensional shape and structure of the uterus. Through finite element analysis, the stress distribution, strain state, and deformation degree of the uterine structure can be comprehensively evaluated. This helps doctors more deeply understand the mechanical properties of the uterus in different physiological states and provides more powerful support for the diagnosis and treatment of diseases.

[0064] In a preferred embodiment of the present invention, in the above step 11, the uterine internal image data is obtained by using a hysteroscope, and the color image in the uterine internal image data is grayscale processed to obtain a grayscale image, which may include:

[0065] The color image is converted by applying a grayscale algorithm, and the color value of each pixel point is converted into a grayscale value. Taking the weighted average method as an example, according to the sensitivity of the human vision to colors, the three RGB components are weighted and averaged to obtain a grayscale image. The specific formula is: ; where 、 、 are the values of the red, green, and blue components respectively. The processed grayscale image is saved in a suitable format (such as JPEG, PNG, etc.).

[0066] In a preferred embodiment of the present invention, in the above step 12, edge detection is performed on the grayscale image to identify the edge contours of the uterus and its cornua, and according to the geometric features of the cornua, including curvature change, angle sharpness, or specific positions on the edge contour, candidate positions of the cornu points are extracted, which may include:

[0067] Step 122, perform edge detection on the grayscale image to identify the edges of the uterus and its cornua to obtain an edge detection result;

[0068] Step 123, extract the contours of the uterus and its cornua according to the edge detection result;

[0069] Step 124, traverse the contours of the uterus and its cornua, and set judgment conditions according to the geometric features of the cornua, including curvature change, angle sharpness, or specific positions on the contour, to obtain an image marked with candidate positions of the cornu points and a candidate position list;

[0070] Step 125, re-mark each point in the candidate position list to obtain a final marked image, and extract the candidate positions of the cornu points from the final marked image.

[0071] In the embodiments of the present invention, based on the characteristics of the grayscale image, the Canny edge detection algorithm is selected. According to the specific situation of the image, the parameters of the Canny algorithm are adjusted, such as the standard deviation of the Gaussian filter, the high and low thresholds, etc. The Canny edge detection algorithm is applied to the grayscale image to identify the edges of the uterus and uterine horns. A binary image is generated, in which the edge part is marked as white (or high brightness), and the background is black (or low brightness). Based on the edge detection result, the contour tracking algorithm is applied to extract the contours of the uterus and uterine horns from the edge detection result. An image containing only the contours of the uterus and uterine horns is generated, in which the contours are clearly marked and the background is black or low brightness. According to the geometric features of the uterine horns, such as curvature changes, sharp angles, or specific positions on the contour, a series of judgment conditions are set. These conditions can be calculations based on mathematical formulas, such as curvature, angle, etc., or threshold settings based on experience.

[0072] Traverse the extracted contour point by point, and apply the judgment conditions to each point. When a certain point meets the judgment conditions, it is marked as a candidate position for the uterine horn point. The candidate position can be marked on the original image, the contour image, or a new image, using different colors or shapes to represent it. A list containing all candidate positions is generated, and each element in the list contains the coordinates of the candidate position and other relevant information, and an image marked with the candidate positions of the uterine horn points is generated. According to the actual morphology and positional relationship of the uterine horn points, screening criteria are formulated. These criteria can be constraint conditions based on distance, angle, shape, etc., used to remove candidate positions that do not conform to the characteristics of the actual uterine horn points. The candidate position list is screened to remove points that do not meet the criteria. On the original image, the contour image, or a new image, the final marking of the screened candidate positions is performed. Use eye-catching colors or shapes to represent the final positions of the uterine horn points. The final marked image is obtained, in which the positions of the two uterine horn points inside the uterus are clearly marked. The final candidate positions of the uterine horn points are extracted from the final marked image.

[0073] Suppose there is a grayscale image of the inside of the uterus obtained by hysteroscopy. The following is the recognition process:

[0074] Select the Canny edge detection algorithm to perform edge detection on the grayscale image to obtain a binary image containing the edges of the uterus and uterine horns. Apply the contour tracking algorithm to extract the contours of the uterus and uterine horns to obtain an image containing only the contours. Find the points with obvious curvature changes and sharp angles on the contour. Apply the judgment conditions to each point. Mark the points that meet the conditions as candidate positions for the uterine horn points to obtain a candidate position list and an image marked with the candidate positions. The distance between the two candidate positions should be within a certain range, and the formed angle should be close to the actual angle of the uterine horn. Apply the screening criteria to screen the candidate positions. Perform the final marking of the screened candidate positions on the original image. Extract the final candidate positions of the uterine horn points from the final marked image.

[0075] In another preferred embodiment of the present invention, in step 122 above, edge detection is performed on the grayscale image to identify the edges of the uterus and uterine horns to obtain an edge detection result, which may include:

[0076] Step 1223, traverse each pixel in the grayscale image, calculate the change in the grayscale values of the pixels around each pixel, and generate the grayscale value change information for each pixel;

[0077] Step 1224, according to a preset threshold, determine whether each pixel is an edge point to obtain an edge point determination result;

[0078] Step 1225, generate a binary image according to the edge point determination result. In the binary image, white pixels represent the detected edge points, and black pixels represent non-edge regions, and use the binary image as the edge detection result.

[0079] In an embodiment of the present invention, each pixel in the grayscale image is traversed using an image processing algorithm (such as the Sobel operator). The Sobel operator is a gradient filter used to calculate the gradient value of each pixel in the image.

[0080] For each pixel, calculate the change in the grayscale values of the pixels around it. This is achieved through convolution operations, that is, using a specific filter (such as the gradient filter of the Sobel operator) to convolve with the image. The convolution kernel of the Sobel operator usually includes two components in the horizontal and vertical directions, which are respectively used to calculate the gradients of the pixel in the horizontal and vertical directions. Specifically, for a pixel point (x, y) in the grayscale image, its gradient in the horizontal direction and the gradient in the vertical direction can be calculated by the following formulas: ; ; where , ..., respectively represent the pixel values within the 3x3 neighborhood around the pixel point . Determine the edge point. According to a preset threshold T, determine whether the gradient value of each pixel exceeds the threshold. If the gradient value ( and , the square root of the sum of squares, that is, the gradient magnitude) exceeds the threshold T, then the pixel is considered an edge point; otherwise, it is a non-edge point. Specifically, for the pixel point , its gradient magnitude can be calculated by the following formula: ; Then, Compare with the threshold T to determine whether the pixel is an edge point. Generate a binary image according to the edge point judgment result. In the binary image, white pixels represent the detected edge points (pixels with gradient values exceeding the threshold), and black pixels represent non-edge regions (pixels with gradient values not exceeding the threshold). Through the binary image, the edge contours in the image can be clearly seen.

[0081] Taking a certain grayscale image as an example, assume the size of the grayscale image is 512 512 pixels, and use the Sobel operator for edge detection. The Sobel operator is a gradient filter, and its convolution kernel usually includes two components in the horizontal and vertical directions. By performing a convolution operation between the convolution kernel of the Sobel operator and the grayscale image, the gradient values of each pixel in the horizontal and vertical directions can be calculated. Set a threshold (such as 100), and determine whether the gradient value of each pixel exceeds this threshold. If the gradient value of a certain pixel is greater than 100, then this pixel is considered an edge point; otherwise, it is a non-edge point. Generate a 512x512 binary image according to the edge point judgment result. In the binary image, white pixels represent the detected edge points (pixels with gradient values greater than 100), and black pixels represent non-edge regions (pixels with gradient values less than or equal to 100).

[0082] By traversing each pixel in the grayscale image and calculating the change in the grayscale values of its surrounding pixels, the edge positions of the uterus and uterine horns can be accurately located. This accurate edge positioning helps to improve the accuracy of subsequent image analysis. According to the edge point judgment result, a binary image can be generated, where white pixels represent the detected edge points and black pixels represent non-edge regions. Through the binary image obtained by edge detection, it is easier to extract feature information such as the shape and size of the uterus and uterine horns. The edge detection result is presented in the form of a binary image, providing an intuitive visualization effect, which helps doctors to more accurately judge the morphological structure of the uterus and uterine horns and whether there are abnormalities. The edge detection algorithm can be integrated into an automated detection system to realize functions such as automatic traversal of grayscale images, edge point judgment, and binary image generation, improving the detection efficiency and accuracy.

[0083] In a preferred embodiment of the present invention, in the above step 13, screen and verify the candidate positions to determine the final uterine horn point position; in the above step 14, taking the final uterine horn point position as a reference, establish a geometric coordinate system corresponding to the image coordinate system, and automatically identify and determine the coordinates of the two uterine horn points inside the uterus, which may include:

[0084] Edge detection is performed on the grayscale image through image processing algorithms (such as Sobel operator, Canny edge detection, etc.) to obtain a preliminary set of edge points. According to the anatomical structure characteristics of the uterus and uterine horns, certain screening conditions (such as shape, size, position, etc.) are set to screen out the candidate positions of the uterine horn points from the set of edge points. For each candidate position, the surrounding image features (such as grayscale value, texture, shape, etc.) are extracted.

[0085] The support vector machine (SVM) algorithm is used to classify the extracted features. The extracted feature vectors are used as inputs to construct an SVM classifier. Using the known anatomical structure data of the uterus and uterine horns as training samples, the SVM classifier is trained. Feature extraction is performed on the candidate positions and input into the trained SVM classifier for classification to screen out more likely positions of the uterine horn points. The convolutional neural network (CNN) algorithm is used for automatic recognition of the uterine horn points. A CNN model suitable for uterine horn point recognition is constructed and trained using a large amount of uterine and uterine horn image data. Automatic analysis is performed on the grayscale image, and the trained CNN model is used to identify the two uterine horn points inside the uterus. According to the positions of the identified uterine horn points in the image and in combination with the setting of the geometric coordinate system, their coordinate positions in the geometric coordinate system are determined.

[0086] In a preferred embodiment of the present invention, in step 15 above, taking the uterine horn point coordinates and the crossbar length as a reference, the actual distance value between the two uterine horn points is calculated; the calculation formula for the actual distance value between the two uterine horn points is:

[0087] ;

[0088] Wherein, represents the actual distance value between the two uterine horn points; 、 represent the abscissas of the uterine horn points in the geometric coordinate system; 、 represent the ordinates of the uterine horn points in the geometric coordinate system; 、 represent the error correction terms; represents the proportional error correction term.

[0089] In an embodiment of the present invention, through image processing techniques (such as edge detection, contour extraction, and feature point recognition), the coordinates of the two uterine horn points in the geometric coordinate system are obtained, denoted as and .

[0090] According to the actual situation, the error correction terms and These correction terms are used to correct errors caused by image acquisition, processing, or coordinate system settings, etc. Similarly, the scale error correction term is determined according to the actual situation. This correction term is used to adjust the scale error caused by image scaling, lens distortion, or inconsistent measurement units, etc. Substitute the obtained coordinate values, error correction terms, and scale error correction terms into the formula for calculation to obtain the actual distance value between two uterine horn points. 。

[0091] Assume that the coordinates of two uterine horn points have been obtained through image processing technology:

[0092] Coordinates of uterine horn point 1: ; Coordinates of uterine horn point 2: 。

[0093] Meanwhile, the error correction term and the scale error correction term have been determined: ; 。Scale error correction term: Substitute these values into the formula for calculation, calculate the coordinate difference and add the error correction term:

[0094] ; 。

[0095] Calculate the sum of squares: ; ; Sum of squares = + = Take the square root and multiply by the scale error correction term: 。Actual distance 。Therefore, the actual distance value between the two uterine horn points is approximately units.

[0096] Through the error correction terms and , the errors caused by image acquisition, processing, or coordinate system settings, etc. can be corrected, thus improving the measurement accuracy. The scale error correction term can further adjust the scale error caused by image scaling, lens distortion, or inconsistent measurement units, etc., making the measurement result closer to the true value. Each parameter (coordinate value, error correction term, scale error correction term) is set according to the actual situation. By substituting each parameter into the formula, the actual distance value can be directly calculated. The calculated actual distance value can provide objective reference information, which helps them evaluate the structure and state of the uterus more accurately, so as to make more appropriate decisions.

[0097] In a preferred embodiment of the present invention, step 16 of constructing a geometric model of the uterus and uterine horns based on the uterine horn point coordinates, the actual distance value between the two uterine horn points, and the uterine internal image data may include:

[0098] Step 166: Construct the geometric shape of the uterus and uterine horns, including lines and arcs, according to the uterine horn point coordinates and the actual distance value between the two uterine horn points.

[0099] Step 167: Combine the geometric shape of the uterus and uterine horns, including lines and arcs, to construct an initial geometric contour of the uterus and uterine horns.

[0100] Step 168: Adjust the shape and size of the initial geometric contour of the uterus and uterine horns to obtain an adjusted geometric contour of the uterus and uterine horns.

[0101] Step 169: Construct a geometric model of the uterus and uterine horns according to the adjusted geometric contour of the uterus and uterine horns.

[0102] In an embodiment of the present invention, according to the uterine horn point coordinates and and the actual distance value between the two uterine horn points , a line segment connecting the two uterine horn points can be drawn. Assuming that the shape of the uterus is approximately elliptical or other suitable geometric shapes, the uterine horn point coordinates and the actual distance value can be used as references to draw a rough contour of the uterus. This can be done by drawing arcs or other curves that should be as close as possible to the actual shape of the uterus. Combine the drawn line segment and arcs (or other curves) to form an initial geometric contour of the uterus and uterine horns. This contour should be a closed figure that can roughly represent the position and shape of the uterus and uterine horns. According to the uterine internal image data, adjust the shape and size of the initial contour. This can be achieved by moving, scaling, or rotating the points on the contour to make the contour fit more closely to the actual shape of the uterus and uterine horns. During the adjustment process, information such as feature points, edges, or textures in the uterine internal image can be referred to to ensure that the adjusted contour has sufficient accuracy. After completing the adjustment of the shape and size, use the adjusted geometric contour of the uterus and uterine horns as the basis for the geometric model. Use computer-aided design (CAD) software to convert the contour into a three-dimensional geometric model. This model can include information such as the cavity of the uterus, the position and shape of the uterine horns, etc.

[0103] Assume that the following data has been obtained: Uterine horn point coordinates: ; . The actual distance value between the two uterine horn points: (This value is calculated by the actual distance value calculation formula between the two uterine horn points mentioned above). It is shown that the uterus is elliptical and the uterine horns are located at both ends of the ellipse.

[0104] Construct a geometric model of the uterus and uterine horns according to the following steps:

[0105] Draw a line segment connecting two uterine horn points in the coordinate system, that is, connecting point and point .

[0106] Assume that the uterus is elliptical. According to the uterine horn point coordinates and actual distance values, the major axis and minor axis of the ellipse can be estimated. Draw an ellipse such that the two uterine horn points are located at both ends of the ellipse, and the shape and size of the ellipse match the internal image data of the uterus. Combine the drawn line segment and the ellipse to form the initial geometric contour of the uterus and uterine horns. According to the detailed information in the internal image data of the uterus, adjust the initial contour so that the contour fits the actual shape of the uterus and uterine horns more closely. For example, the curvature of the ellipse can be adjusted, points on the contour can be moved, or the size of the contour can be changed, etc. Use CAD software or other tools to convert the adjusted contour into a three-dimensional geometric model. This model should be able to accurately represent information such as the cavity of the uterus, the position and shape of the uterine horns, etc.

[0107] By using the uterine horn point coordinates and actual distance values, the position of the uterine horns can be accurately located, thereby ensuring that the constructed geometric model matches the real uterus and uterine horns in terms of spatial position. Combining the internal image data of the uterus, the shape and size of the model can be further refined to make it closer to the real uterine structure. The constructed geometric model provides an intuitive three-dimensional visualization effect, enabling a clearer observation and understanding of the structure and morphology of the uterus and uterine horns. The geometric model can provide valuable reference information for medical research and diagnosis. For example, when studying problems such as uterine dysplasia and cornual pregnancy, the model can help doctors more accurately evaluate and analyze the condition. By constructing a personalized geometric model of the uterus and uterine horns according to the specific data of each patient, more precise personalized medicine can be achieved. The constructed geometric model can facilitate data analysis and processing, such as calculating parameters such as the volume and surface area of the uterus, or analyzing the angular and positional relationships of the uterine horns, etc.

[0108] In a preferred embodiment of the present invention, in step 17 above, according to the geometric model of the uterus and uterine horns, perform finite element analysis to evaluate the stress distribution, strain state, and deformation degree of the uterine structure, which may include:

[0109] Step 171, define material properties for the uterine tissue, including elastic modulus and Poisson's ratio parameters;

[0110] Perform mesh division on the geometric model of the uterus and uterine horns, and divide the geometric model of the uterus and uterine horns into multiple small, interconnected units;

[0111] Step 172: Set boundary conditions and apply loads to the uterine and uterine horn geometric models, including simulating pressure changes within the uterus or external forces.

[0112] Step 173: Perform finite element analysis on the uterine and uterine horn geometric models using a solver. During the analysis, the solver calculates the stress, strain, and deformation of each element based on the set boundary conditions, loads, and the material properties of the uterine tissue to obtain finite element analysis result data, including stress distribution diagrams, strain state diagrams, and deformation degree diagrams.

[0113] Step 174: Analyze the stress distribution diagrams, strain state diagrams, and deformation degree diagrams to evaluate the stress distribution, strain state, and deformation degree of the uterine structure.

[0114] In the embodiments of the present invention, material properties are defined for the uterine tissue, including the elastic modulus (indicating the ease of elastic deformation of the material under force) and the Poisson's ratio parameter (indicating the proportional relationship between transverse deformation and longitudinal deformation of the material under force). The uterine and uterine horn geometric models are meshed, dividing the models into multiple small, interconnected elements. These elements are the basic computational units for finite element analysis. By calculating the stress, strain, and deformation of each element, the stress distribution, strain state, and deformation degree of the entire model can be obtained. Boundary conditions are set for the uterine and uterine horn geometric models, such as fixing certain parts of the model to simulate the fixed state of the uterus in the body. Loads are applied, including simulating pressure changes within the uterus (such as pressure caused by fetal growth, increased amniotic fluid, etc.) or external forces (such as external forces on the abdomen).

[0115] Perform finite element analysis on the uterine and uterine horn geometric models using a solver. The solver calculates the stress, strain, and deformation of each element based on the set boundary conditions, loads, and the material properties of the uterine tissue. During the analysis, the solver iteratively solves the equations until a convergent solution is obtained. After obtaining the finite element analysis result data, stress distribution diagrams, strain state diagrams, and deformation degree diagrams can be generated. Analyze the stress distribution diagrams, strain state diagrams, and deformation degree diagrams to evaluate the stress distribution, strain state, and deformation degree of the uterine structure. For example, it can be observed which areas have greater stress, which areas have more obvious deformation, and whether the strain state is within a safe range, etc.

[0116] Suppose it is necessary to evaluate the stress distribution, strain state, and deformation degree of a pregnant woman's uterus during the second trimester of pregnancy. The following steps are carried out:

[0117] Define appropriate elastic modulus and Poisson's ratio parameters for uterine tissue based on medical literature or experimental data. For example, assume the elastic modulus is 10 kPa and the Poisson's ratio is 0.45. Mesh the geometric model of the uterus and uterine horns using finite element analysis software. To ensure calculation accuracy and efficiency, an appropriate mesh density and element type can be selected. For example, tetrahedral elements can be used for meshing, and ensure a higher mesh density in key areas (such as uterine horns, uterine walls, etc.). Set boundary conditions, fix the bottom of the uterus to simulate its fixed state in the body. Apply loads to simulate the pressure changes in the uterus. For example, it can be assumed that due to fetal growth and increased amniotic fluid, the pressure in the uterus gradually increases. Use a solver to perform finite element analysis on the geometric model of the uterus and uterine horns. During the analysis, the solver will calculate the stress, strain, and deformation of each element and generate corresponding result data. Generate stress distribution diagrams, strain state diagrams, and deformation degree diagrams based on the finite element analysis result data. Observing these diagrams can reveal that: the stress is relatively large in the bottom and uterine horn regions of the uterus; certain deformations have occurred in the uterine wall; the overall strain state is within the safe range but attention needs to be paid to monitoring, etc. This information can provide valuable references for evaluating the uterine health status of pregnant women and formulating appropriate medical measures.

[0118] Through finite element analysis, the stress distribution, strain state, and deformation degree of the uterus under specific conditions can be obtained. The data provided by finite element analysis can help doctors more accurately evaluate the health status of the uterus, thereby formulating more appropriate medical decisions. For example, in uterine surgery planning, doctors can use the results of finite element analysis to predict the deformation and stress distribution of the uterus during the operation, so as to optimize the surgical plan and reduce the surgical risk. By constructing personalized geometric models of the uterus and uterine horns for each patient and performing finite element analysis, we can achieve more precise personalized medicine. By simulating different physiological and pathological conditions, researchers can explore the relationship between the mechanical properties of the uterus and the occurrence and development of diseases, providing new ideas and methods for disease prevention and treatment.

[0119] In another preferred embodiment of the present invention, step 174, analyzing the stress distribution diagram, strain state diagram, and deformation degree diagram to evaluate the stress distribution, strain state, and deformation degree of the uterine structure, may include:

[0120] Step 1745, analyze the stress distribution diagram, strain state diagram, and deformation degree diagram to identify high-stress regions and low-stress regions, stress gradient changes, and the magnitude and direction of strain and deformation, so as to obtain the analysis results of the stress distribution, strain state, and deformation degree diagrams;

[0121] Step 1746: Based on the analysis results of the stress distribution, strain state, and deformation degree diagrams, comprehensively evaluate the uterine structure, identify problem areas, including stress concentration, excessive strain, or abnormal deformation, to assess the stress distribution, strain state, and deformation degree of the uterine structure.

[0122] In the embodiments of the present invention, by observing the stress distribution diagram, it can be clearly seen which areas in the uterus are subjected to greater stress (high stress areas) and which areas are subjected to less stress (low stress areas). High stress areas may be weak points or vulnerable parts in the uterine structure and require special attention.

[0123] The stress gradient represents the rate of change of stress in the uterine structure. By observing the stress distribution diagram, the change of the stress gradient can be analyzed to understand whether the stress distribution in the uterus is uniform and whether there are stress mutations. Through the strain state diagram and the deformation degree diagram, the deformation of the uterus under the stress state can be understood. By observing the direction and magnitude of the strain and deformation, it can be judged whether the uterus has abnormal deformation or torsion, and whether these deformations have an impact on the function of the uterus. Based on the analysis results of the stress distribution, strain state, and deformation degree diagrams, comprehensively evaluate the uterine structure. Identify problem areas such as stress concentration, excessive strain, or abnormal deformation, which may be potential risk points in the uterine structure.

[0124] Suppose we are evaluating the stress distribution, strain state, and deformation degree of a pregnant woman's uterus in the late pregnancy. The following is the analysis process:

[0125] By observing the stress distribution diagram, it is found that the bottom and cornual regions of the uterus are subjected to greater stress, while the middle part of the uterine wall is subjected to less stress. This indicates that the bottom and cornual regions of the uterus may be weak points in the uterine structure and require special attention. By observing the stress distribution diagram, we find that there are large gradient changes in the stress in the bottom and cornual regions of the uterus, while it is relatively uniform in the middle part of the uterine wall. This indicates that when the uterus is stressed, the bottom and cornual regions are more likely to have stress concentration and damage. Through the strain state diagram and the deformation degree diagram, it is found that the uterus has obvious deformation in the late pregnancy, especially in the bottom and cornual regions. The direction of strain and deformation in these regions mainly points to the outside of the uterus, and the degree of deformation is large. This indicates that the uterus bears greater pressure in the late pregnancy, and the deformation of these regions needs to be concerned. Based on the above analysis results, comprehensively evaluate the uterine structure, and it is found that the bottom and cornual regions of the uterus are potential risk points. These regions may have problems such as stress concentration, excessive strain, or abnormal deformation, and appropriate medical measures need to be taken for intervention and monitoring. For example, it can be recommended that the pregnant woman reduce activities, avoid external impact on the abdomen, and have regular prenatal check-ups to monitor the changes of the uterus.

[0126] Such as Figure 2As shown in the figure, an embodiment of the present invention further provides a finite element analysis system 20 for the visual reference of the uterine cornea of a hysteroscope, including:

[0127] An acquisition module 21, configured to use a hysteroscope to acquire internal uterine image data, perform grayscale processing on the color image in the internal uterine image data to obtain a grayscale image; perform edge detection on the grayscale image, identify the edge contours of the uterus and the uterine cornea, and extract the candidate positions of the uterine cornea points according to the geometric features of the uterine cornea, including curvature changes, angle sharpness, or specific positions on the edge contour; screen and verify the candidate positions to determine the final position of the uterine cornea points.

[0128] A processing module 22, configured to establish a geometric coordinate system corresponding to the image coordinate system based on the final position of the uterine cornea points, and automatically identify and determine the coordinates of two uterine cornea points inside the uterus; calculate the actual distance value between the two uterine cornea points based on the coordinates of the uterine cornea points and the crossbar length; construct a geometric model of the uterus and the uterine cornea according to the coordinates of the uterine cornea points, the actual distance value between the two uterine cornea points, and the internal uterine image data; perform finite element analysis according to the geometric model of the uterus and the uterine cornea to evaluate the stress distribution, strain state, and deformation degree of the uterine structure.

[0129] It should be noted that this system corresponds to the above method. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0130] An embodiment of the present invention further provides a computing device, including: a processor and a memory storing a computer program. When the computer program is run by the processor, it executes the method described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0131] An embodiment of the present invention further provides a computer-readable storage medium storing instructions. When the instructions are run on a computer, the computer is made to execute the method described above. All implementation manners in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0132] The above are the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A hysteroscopic uterine angle visual benchmark finite element analysis method, characterized in that: The method comprises: Acquiring internal image data of the uterus by using a hysteroscope, and graying a color image in the internal image data of the uterus to obtain a gray image; Perform edge detection on the grayscale image to identify the edge contours of the uterus and uterine horns, and extract candidate positions of uterine horn points based on the geometric features of the uterine horns, including curvature changes, sharp angles, or specific positions on the edge contours; Screen and verify the candidate positions to determine the final palace corner point position; Taking the final uterine horn point position as a reference, a geometric coordinate system corresponding to the image coordinate system is established to automatically identify and determine the coordinates of the two uterine horn points inside the uterus; Taking the palace corner point coordinates and the length of the horizontal bar as the basis, calculate the actual distance value between the two palace corner points; Constructing a geometric model of the uterus and uterine horns according to the uterine horn point coordinates, the actual distance value between the two uterine horn points, and the internal image data of the uterus, including: constructing geometric shapes of the uterus and uterine horns, including lines and arcs, according to the uterine horn point coordinates and the actual distance value between the two uterine horn points; combining the geometric shapes of the uterus and uterine horns, including lines and arcs, to construct an initial geometric contour of the uterus and uterine horns; adjusting the shape and size of the initial geometric contour of the uterus and uterine horns to obtain an adjusted geometric contour of the uterus and uterine horns; constructing a geometric model of the uterus and uterine horns according to the adjusted geometric contour of the uterus and uterine horns; According to the geometric model of the uterus and uterine horns, finite element analysis is performed to evaluate the stress distribution, strain state and deformation degree of the uterine structure, including: defining material properties for uterine tissue, including elastic modulus and Poisson's ratio parameters; meshing the geometric model of the uterus and uterine horns, dividing the geometric model of the uterus and uterine horns into multiple small, interconnected units; setting boundary conditions and applying loads to the geometric model of the uterus and uterine horns, including simulating pressure changes in the uterus or external forces; performing finite element analysis on the geometric model of the uterus and uterine horns through a solver. During the analysis, the solver calculates the stress, strain and deformation of each unit according to the set boundary conditions and loads and the material properties of the uterine tissue to obtain finite element analysis result data, including stress distribution diagrams, strain state diagrams and deformation degree diagrams; analyzing the stress distribution diagrams, strain state diagrams and deformation degree diagrams to evaluate the stress distribution, strain state and deformation degree of the uterine structure.

2. The hysteroscopic uterine angle visual reference finite element analysis method according to claim 1, characterized in that: Perform edge detection on the grayscale image to identify the edge contours of the uterus and uterine horns, and extract candidate positions of uterine horns based on the geometric features of the uterine horns, including curvature changes, sharp angles, or specific positions on the edge contours, including: Perform edge detection on the grayscale image to identify the edges of the uterus and uterine cornu to obtain edge detection results; According to the edge detection results, the contours of the uterus and uterine horns are extracted; Traversing the contours of the uterus and uterine horns, and setting judgment conditions according to the geometric features of the uterine horns, including curvature changes, sharp angles, or specific positions on the contours, to obtain an image marked with candidate positions of uterine horn points and a list of candidate positions; Each point in the candidate position list is marked again to obtain a final marked image, and the candidate position of the palace corner point is extracted from the final marked image.

3. The hysteroscopic uterine angle visual reference finite element analysis method according to claim 2, characterized in that: Perform edge detection on the grayscale image to identify the edges of the uterus and uterine horns to obtain edge detection results, including: Traverse each pixel in the grayscale image, calculate the grayscale value change of the pixels around each pixel, and generate the grayscale value change information of each pixel; According to the preset threshold, determine whether each pixel is an edge point, and obtain the edge point determination result; A binary image is generated according to the edge point judgment result, in which white pixels represent detected edge points and black pixels represent non-edge areas, and the binary image is used as the edge detection result.

4. The hysteroscopic uterine angle visual reference finite element analysis method according to claim 3, characterized in that: The actual distance between two palace corner points is calculated as follows: ； in, Indicates the actual distance between two palace corner points; , It represents the horizontal coordinate of the palace corner point in the geometric coordinate system; , It represents the ordinate of the palace corner point in the geometric coordinate system; , Respectively expressed in Direction and Scaling factor in direction; represents the overall scaling factor; , Respectively expressed in Direction and The offset in direction; , Respectively expressed in Direction and Noise term in the direction.

5. The hysteroscopic uterine angle visual reference finite element analysis method according to claim 4, characterized in that: The stress distribution diagram, strain state diagram and deformation degree diagram are analyzed to evaluate the stress distribution, strain state and deformation degree of the uterine structure, including: Analyze the stress distribution diagram, strain state diagram and deformation degree diagram, identify high stress areas and low stress areas, stress gradient changes, strain and deformation direction, and obtain the analysis results of stress distribution, strain state and deformation degree diagram; Based on the analysis results of the stress distribution, strain state and deformation degree diagrams, a comprehensive assessment of the uterine structure is conducted to identify problem areas, including stress concentration, excessive strain or abnormal deformation, in order to assess the stress distribution, strain state and deformation degree of the uterine structure.

6. A hysteroscopic uterine angle visual reference finite element analysis system, the system implements the method according to claim 1, characterized in that: include: An acquisition module is used to acquire the internal image data of the uterus by using a hysteroscope, and grayscale the color image in the internal image data of the uterus to obtain a grayscale image; Perform edge detection on the grayscale image to identify the edge contours of the uterus and uterine horns, and extract candidate uterine horn locations based on the geometric features of the uterine horns, including curvature changes, sharp angles, or specific locations on the edge contours; screen and verify the candidate locations to determine the final uterine horn location; The processing module is used to establish a geometric coordinate system corresponding to the image coordinate system based on the final uterine horn point position, automatically identify and determine the coordinates of the two uterine horn points inside the uterus; use the uterine horn point coordinates and the crossbar length as a reference to calculate the actual distance value between the two uterine horn points; construct a geometric model of the uterus and uterine horns based on the uterine horn point coordinates, the actual distance value between the two uterine horn points and the internal image data of the uterus; perform finite element analysis based on the geometric model of the uterus and uterine horns to evaluate the stress distribution, strain state and deformation degree of the uterine structure.

7. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

Images