Cesarean section training method and system based on mixed reality

Abstract

The invention discloses a caesarean section training method and system based on mixed reality, and the method comprises the steps: obtaining the medical image data of the pelvis and abdomen of a pregnant woman, recognizing and marking the boundary and region of a key tissue structure, constructing a three-dimensional model of the key tissue structure, and generating key indexes of caesarean section training. Importing the constructed three-dimensional model into an MR system, collecting action data, generating a real-time feedback prompt, receiving a training ending signal, generating an evaluation result, recording operations which do not conform to an evaluation standard, and providing targeted improvement suggestions for trainees. According to the method, the quality and efficiency of operation training can be effectively improved, a safe, efficient and immersive training platform is provided for trainees through precise processing of medical images, efficient reconstruction of three-dimensional models, real-time feedback and personalized operation evaluation, personalized guidance can be carried out according to the performance of the trainees, and the training efficiency of the trainees is improved. And the training effect of surgical skills is further improved.

Description

[Technical field]

[0001] The present invention relates to the field of mixed reality technology, and in particular to a cesarean section training method and system based on mixed reality. [Background technology]

[0002] With the continuous development of medical technology, cesarean section, as a common and important obstetric and gynecological surgery, is widely used to solve the life safety problems of mothers and fetuses. However, the complexity of cesarean section requires doctors to have high professional skills and rich experience. Traditional surgical training methods mainly rely on actual operations at the surgical site, which are limited by factors such as training time, individual differences in patients, and operational risks, and cannot provide sufficient practice opportunities.

[0003] In recent years, mixed reality (MR) technology has achieved significant applications in medical education. Mixed reality technology can integrate the virtual environment with the real world to provide trainees with a highly immersive and interactive training experience, which is particularly suitable for simulation training of complex surgeries. Despite this, existing mixed reality surgical training methods still face many challenges, such as how to accurately extract key tissue structures from imaging data, how to ensure the realism and safety of the training process, and how to provide real-time feedback and evaluation of trainees' operations.

[0004] Therefore, there is an urgent need for a cesarean section surgery training method and system based on mixed reality, which can help trainees quickly master surgical skills and improve training effectiveness through precise image data analysis, real-time operation feedback and evaluation. [Summary of the invention]

[0005] In view of this, an embodiment of the present invention provides a cesarean section surgery training method and system based on mixed reality.

[0006] In a first aspect, an embodiment of the present invention provides a cesarean section training method based on mixed reality, the method comprising:

[0007] S1. Obtain medical imaging data of the pelvis and abdomen of pregnant women, extract characteristic values ​​of key tissue structures after preprocessing, and identify and mark the boundaries and regions of key tissue structures;

[0008] S2, initially segmenting the key tissue structure from the image data, then performing fine segmentation, and using a reconstruction algorithm to construct a three-dimensional model of the key tissue structure;

[0009] S3. Calculate the reference evaluation value of cesarean section training based on the characteristic values ​​of the key tissue structures, and generate key indicators of cesarean section training;

[0010] S4, converting the constructed 3D model into a format recognizable by the mixed reality system to import into the MR system, and performing scene setting and spatial registration;

[0011] S5, receiving a training start signal, detecting the trainee's surgical operation to collect motion data, comparing the trainee's operation data with key indicators, generating real-time feedback prompts, receiving a training end signal, and generating evaluation results;

[0012] S6. Record the operations that do not meet the evaluation criteria, provide targeted improvement suggestions to trainees, and update the trainees’ score curve.

[0013] According to the above aspects and any possible implementation, an implementation is further provided, wherein S1 specifically includes:

[0014] Obtain three-dimensional volume data D(x, y, z) of the pregnant woman's pelvis and abdomen, where x, y, and z represent spatial coordinates, and D represents the intensity value of each voxel;

[0015] Apply an anisotropic diffusion filter F AD Reduce the noise to get D f (x, y, z), for D f (x, y, z) is normalized to obtain D n (x, y, z), use histogram equalization to enhance the contrast and get D e (x, y, z);

[0016] The preprocessed image data D e (x, y, z) for feature extraction, specifically: calculating the grayscale histogram, mean and variance statistical features of the image, using the Sobel operator to calculate the gradient map of the image to obtain edge information, and using the grayscale co-occurrence matrix to extract texture features;

[0017] Determine the area where the key tissue structure is located according to the extracted features, wherein the key tissue structure list includes: uterus (U): including uterine wall and uterine cavity, placenta (P): positioning and range, fetal head (F): position and orientation, bladder (B): to avoid damage during surgery, abdominal wall layer (A): skin, subcutaneous tissue, fascia and muscle;

[0018] The gradient image is nonlinearly enhanced to enhance the edge information, and the region of interest is determined by combining the enhanced edge information and the key tissue structure region.

[0019] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein S2 specifically includes:

[0020] Perform initial segmentation using the determined region of interest as an initial condition to generate a preliminary segmentation mask;

[0021] According to the characteristics of the tissue structure, the segmentation algorithm is selected again for segmentation, and the accurate segmentation mask is generated by iterative solution, and the segmentation result is post-processed;

[0022] The Marching Cubes algorithm was used to convert the segmentation mask into a three-dimensional model of key tissue structures and perform model optimization.

[0023] According to the above aspects and any possible implementation, an implementation is further provided, wherein S3 specifically includes:

[0024] Calculate the volume, surface area and center of mass position of key tissue structures, extract and evaluate tissue compactness, and calculate the distance and relative position between tissues;

[0025] Calculate the incision position and operation force reference value, and set the operation time reference value;

[0026] Generate key indicators for surgical training: allowable incision position error, operating force range and surgical time limit.

[0027] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein S4 specifically includes:

[0028] Convert the constructed three-dimensional model from the original format to the target format required by the mixed reality system MR, and convert the coordinate system of the three-dimensional model to the coordinate system used by the MR system;

[0029] Build a virtual operating room scene in the MR system, including the following elements: operating table: set the size and position of the operating table; medical instruments: surgical instrument models; lighting settings: configure ambient light and spotlight sources; simulate the lighting conditions of the operating room; other scene elements: walls and floors;

[0030] Importing the converted 3D model into the virtual scene of the MR system, and adjusting the position, rotation, and scaling parameters of the model to match the virtual operating room environment;

[0031] Designing a user interaction interface in the MR system, including operation instructions, information display and feedback prompts. The operation instructions are used to display surgical steps and precautions; the information display is used to display the trainee's operation data and evaluation results in real time; the feedback prompts are used to provide visual, auditory or tactile feedback to indicate operation errors or successes;

[0032] An interaction mode is set in the MR system, including gesture recognition, voice control and handle operation, wherein the gesture recognition is used to support the movement and rotation of the gesture control model, the voice control is used to interact with the system through voice commands, and the handle operation is used to interact using a handle or controller;

[0033] Setting rendering effects in the MR system, including material properties, lighting effects, shadows and reflections, and texture maps, wherein the material properties are used to set the reflectivity and transparency material parameters of the model, the lighting effects are used to adjust the intensity, color, and position of the light source, the shadows and reflections are used to enable shadow and reflection effects, and the texture maps are used to apply high-resolution texture maps;

[0034] Define the real space coordinate system: a coordinate system based on the real world, in which all positions and postures in the real environment are expressed; define the virtual space coordinate system: a coordinate system of the virtual model in the MR system;

[0035] Select a set of corresponding landmarks in the real space and virtual space respectively to establish a real space landmark set Establishing a virtual space landmark set The rigid body transformation matrix T = {R, Τ} is calculated using the least squares method, so that Calculate the centroid of virtual and real landmarks Decentralized Coordinates Calculate the covariance matrix Perform singular value decomposition of the covariance matrix H H = UΣV Τ , calculate the rotation matrix R = VU Τ , calculate the translation vector V a =R·V v +Τ, calculate the root mean square error Among them, P i r represents the coordinates of the i-th landmark point in the real space, P i v represents the coordinates of the corresponding i-th marker point in the virtual space, N represents the number of marker points, U, Σ and V represent the SVD decomposition results of the covariance matrix, R represents the rotation matrix, Τ represents the translation vector, V v Represents the vertex coordinate matrix of the virtual model, V a represents the vertex coordinate matrix after registration, ||·|| represents the Euclidean distance, and sets the registration error threshold. If the root mean square error is less than the registration error threshold, the registration is considered successful; otherwise, re-registration is required.

[0036] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein S5 specifically includes:

[0037] Configure sensor equipment to collect real-time operation data such as the position and force of the trainee during the surgical training process. The sensor equipment includes a position sensor and a force sensor. The position sensor is used to capture the position and posture of the trainee's hand and surgical instrument in real time, and the force sensor is used to measure the force applied by the trainee during the operation.

[0038] Receive the training start signal, pre-process the collected operation data, and extract key features;

[0039] The trainee's real-time operation data is compared with the reference evaluation value, and a real-time feedback prompt is generated. The position deviation warning threshold is set and set to 80% of the maximum allowable value. When the position deviation is judged to exceed the position deviation warning threshold, a warning is triggered, a warning message is displayed on the interface, a prompt sound is played, and a warning event is recorded; the force deviation warning threshold is set and set to 80% of the maximum allowable value. When the force deviation is judged to exceed the force deviation warning threshold, a warning is triggered, a warning message is displayed on the interface, a prompt sound is played, and a warning event is recorded;

[0040] Receive the training end signal, calculate and display the trainee's total surgical training score S on the interface 总 , the total score of the surgical training is S 总 The calculation formula is:

[0041] S 总 =w 1 S p +w 2 S f +w 3 S t , w 1 +w 2 +w 3 =1,

[0042]

[0043]

[0044] Among them, w 1 、w 2 and w 3 represents the weight coefficient, S p Indicates the incision location score, S f Indicates the operation strength score, S t represents the surgical time score, ΔP represents the Euclidean distance between the trainee’s actual incision position and the reference incision position, δF represents the force tolerance, and F a It represents the average force applied by the trainee during the operation, F r Indicates the reference strength, T u represents the actual time taken by the trainee to complete the operation, δT represents the time tolerance, and Tr Indicates reference time;

[0045] Set the passing score threshold S 1 , and set the excellent score threshold S 2 , if S 总 >S 2 The evaluation is "excellent", if S 1 ≤S 总 <S 2 The evaluation is "qualified", if S 总 <S 1 It is evaluated as “unqualified”;

[0046] Generate and display evaluation results.

[0047] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein S6 specifically includes:

[0048] Record the trainee's hand or instrument position trajectory throughout the training process, record the change of the trainee's operating force over time, record the time of each data point, and save the data in a structured format;

[0049] Receive the training end signal, compare the trainee's operation data with key indicators to analyze the cause of the error;

[0050] Classify the types of operation errors made by trainees during the training process, including positioning deviation errors where the position deviation exceeds the position deviation warning threshold, force control errors where the force deviation exceeds the force deviation warning threshold, operation process errors where the surgical steps are wrong, and time errors that exceed the time threshold, and calculate the number of different types of operation errors made by trainees during the training process;

[0051] Provides a positioning practice module for positioning deviation errors, including positioning tasks of different difficulty levels;

[0052] Provides a strength training module to address strength control errors, including simulated tissue training tasks of different hardness;

[0053] Provide video modules for operation process errors and time errors, including learning tasks of standard cesarean section operation process;

[0054] According to the ID file established for the trainee, the total score of each surgical training of the corresponding trainee is used as a data point to update and generate the trainee's score curve.

[0055] According to the above aspects and any possible implementation manner, an implementation manner is further provided, wherein the method further includes:

[0056] Set an initial warning threshold D for position deviation at the beginning of training p0The initial warning threshold D of the operation intensity f0 ;

[0057] According to the total training time T t The training process is divided into multiple time periods, each of which is T long. s ,At the end of each time period, the trainee's operation compliance rate in that time period is judged. If the operation compliance rate reaches the preset standard, the warning threshold is updated and the next time period is entered;

[0058] Update the warning threshold using a dynamic warning threshold update formula;

[0059] In each time period, the trainee's operation deviation is detected in real time. If the position force deviation exceeds the position deviation warning threshold in the current time period, D p (t), or when the force deviation exceeds the force deviation warning threshold in the current time period D f (t), immediately give a feedback tone, highlight the specific value of the deviation on the user interface, and suggest trainees to adjust their operations. If the deviation exceeds the threshold more than 3 times within a certain time period, the system will record the time period as not meeting the standard;

[0060] If N consecutive 1 The operation compliance rate in the time period is higher than the first preset compliance rate Q 1 , then the threshold attenuation rate constant in the dynamic warning threshold update formula is adjusted, that is, λ and γ are increased by 20%. 2 The operation compliance rate in the time period is lower than Q 2 , then relax the threshold adjustment rate, that is, reduce λ and γ by 10% respectively, where N 1 >N 2 And Q 1 >Q 2 ;

[0061] In the total training time T t When λ and γ are 80% of their initial values, they are reduced to 50% of their initial values ​​respectively;

[0062] At the end of the training, the system generates an operational stability report based on the deviation records of the entire training process, and statistics the average, standard deviation and compliance rate of position and force deviations.

[0063] According to the above aspects and any possible implementation, an implementation is further provided, wherein the dynamic warning threshold update formula includes a dynamic update formula for a position deviation threshold and a dynamic update formula for a force deviation threshold, wherein:

[0064] The dynamic update formula of the position deviation threshold is:

[0065] The dynamic update formula of the force deviation threshold is:

[0066] Among them, D p (t) represents the position deviation warning threshold at time t, D f (t) represents the force deviation warning threshold at time t, D p0 Indicates the initial position deviation warning threshold, D f0 represents the force deviation warning threshold, k and m represent the dynamic adjustment coefficients of position and force feedback, T t Represents the total length of training time, T s represents the duration of a single time segment, and λ and γ represent the decay rate constants of position and strength.

[0067] In a second aspect, an embodiment of the present invention provides a cesarean section surgery training system based on mixed reality, the system comprising:

[0068] An image data acquisition module, used to acquire medical image data of the pelvis and abdomen of a pregnant woman;

[0069] A preprocessing module, used for preprocessing the acquired medical image data;

[0070] A key organizational structure extraction module is used to extract the characteristic values ​​of the key organizational structure, identify and mark the boundaries and regions of the key organizational structure;

[0071] A 3D model building module is used to initially segment key tissue structures from image data, then perform fine segmentation, and use a reconstruction algorithm to build a 3D model of the key tissue structure;

[0072] An indicator generation module, used for calculating a reference evaluation value of cesarean section training based on characteristic values ​​of key tissue structures, and generating key indicators of cesarean section training;

[0073] A mixed reality conversion module is used to convert the constructed 3D model into a format recognizable by the mixed reality system so as to import it into the MR system, and perform scene setting and spatial registration;

[0074] The training analysis module is used to receive a training start signal, detect the trainee's surgical operation to collect action data, compare the trainee's operation data with key indicators, generate real-time feedback prompts, receive a training end signal, and generate evaluation results;

[0075] The evaluation feedback module is used to record operations that do not meet the evaluation standards, provide targeted improvement suggestions to trainees, and update and generate the trainees' score curve.

[0076] One of the above technical solutions has the following beneficial effects:

[0077] The present invention proposes a cesarean section surgery training method and system based on mixed reality. The present invention can effectively improve the quality and efficiency of surgical training. Through precise processing of medical images, efficient reconstruction of three-dimensional models, real-time feedback and personalized operation evaluation, it not only provides trainees with a safe, efficient and immersive training platform, but also can provide personalized guidance based on their performance, thereby further improving the training effect of surgical skills.

Brief Description of the Drawings

[0078] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0079] Figure 1 It is a flow chart of the cesarean section training method S1-S6 based on mixed reality provided in an embodiment of the present invention;

[0080] Figure 2 This is a functional block diagram of a cesarean section surgery training system based on mixed reality provided in an embodiment of the present invention. [Specific implementation method]

[0081] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0082] Please refer to Figure 1 , which is a flow chart of S1-S7 of a cesarean section training method based on mixed reality provided by an embodiment of the present invention, such as Figure 1 As shown, the method comprises the following steps:

[0083] S1. Obtain medical imaging data of the pelvis and abdomen of pregnant women, extract characteristic values ​​of key tissue structures after preprocessing, and identify and mark the boundaries and regions of key tissue structures;

[0084] S2, initially segmenting the key tissue structure from the image data, then performing fine segmentation, and using a reconstruction algorithm to construct a three-dimensional model of the key tissue structure;

[0085] S3. Calculate the reference evaluation value of cesarean section training based on the characteristic values ​​of the key tissue structures, and generate key indicators of cesarean section training;

[0086] S4, converting the constructed 3D model into a format recognizable by the mixed reality system to import into the MR system, and performing scene setting and spatial registration;

[0087] S5, receiving a training start signal, detecting the trainee's surgical operation to collect motion data, comparing the trainee's operation data with key indicators, generating real-time feedback prompts, receiving a training end signal, and generating evaluation results;

[0088] S6. Record the operations that do not meet the evaluation criteria, extract the action feature values ​​to analyze the causes of the errors, provide targeted improvement suggestions for the trainees, and update the trainees’ score curve.

[0089] The embodiments of the present invention can accurately identify and mark key tissue structures by acquiring medical imaging data of the pelvis and abdomen of pregnant women and performing effective preprocessing and feature value extraction; by adopting specific image processing techniques and steps, effective image features can be extracted from noise, ensuring that the boundaries and regions of key tissue structures can be clearly and accurately calibrated, avoiding errors caused by noise or poor image quality; through initial segmentation and fine segmentation techniques, combined with reconstruction algorithms, three-dimensional models of key tissue structures are accurately constructed, which not only displays the geometric morphology of each anatomical structure, but also provides the spatial relationship of the tissue structure, greatly improving the realism and operation accuracy of the virtual scene during training, and through fine segmentation and modeling techniques, it is ensured that the geometric morphology and position of each anatomical structure are highly consistent with the actual situation; through a surgical training system based on mixed reality, trainees can perform surgical operations in a highly simulated virtual environment, thereby effectively avoiding the possible risks in traditional surgical training. The high sense of immersion and real-time feedback mechanism provided by the MR system enables trainees to practice complex surgical operations in a risk-free environment, greatly reducing the risks caused by lack of experience or unskilled skills in actual operations; by comparing the trainees' surgical operation data with key indicators, operational feedback can be generated in real time to help trainees understand the correctness and level of their surgical operations in a timely manner. This real-time evaluation can provide personalized feedback prompts for each trainee's specific operational behavior, thereby improving learning efficiency and avoiding the accumulation of operational errors. It can not only enhance the trainees' self-confidence, but also enhance their understanding and mastery of the surgical process; by recording operations that do not meet the evaluation standards and analyzing the reasons for operational failure, targeted improvement suggestions are provided for each trainee. These suggestions can not only help trainees identify deficiencies in their operations, but also provide corresponding improvement strategies based on the types of operational errors. By generating a score curve for the trainees, the trainees' learning progress can be clearly reflected. The present invention provides a highly simulated and intelligent surgical training platform to help doctors simulate various surgical scenarios in non-real surgical scenarios and improve their actual surgical operation capabilities. Especially for complex or high-risk surgical operations, doctors can practice repeatedly, accumulate experience, and improve their clinical surgical emergency response capabilities.

[0090] Among them, obtaining medical imaging data of the pregnant woman's pelvis and abdomen includes:

[0091] Pregnant women were selected and their informed consent was obtained, and their medical imaging data was used for educational and training purposes. Magnetic resonance imaging (MRI) was used to obtain volumetric data of the pelvis and abdomen, and imaging parameters were set to ensure that the resolution and contrast were sufficient to distinguish key tissue structures, for example: MRI parameters used T1-weighted and T2-weighted sequences with a layer thickness of less than 1 mm; three-dimensional volume data D (x, y, z) was obtained, where x, y, and z represent spatial coordinates and D represents the intensity value of each voxel.

[0092] In a preferred embodiment of the present invention, S1 specifically includes:

[0093] Obtain three-dimensional volume data D(x, y, z) of the pregnant woman's pelvis and abdomen, where x, y, and z represent spatial coordinates, represent the position of voxels, and D represents the intensity value of each voxel;

[0094] Image data preprocessing:

[0095] Apply an anisotropic diffusion filter F AD Reduce the noise to get D f (x, y, z), defined as: D f (x, y, z) = F AD [D(x, y, z)], where D f (x, y, z) represents the image data after noise reduction, F AD Represents the anisotropic diffusion filter operator; based on the Perona-Malik model, the anisotropic diffusion equation is: in, represents the rate of change of image intensity with respect to time, represents the gradient amplitude of the image, k represents the parameter that controls the diffusion rate; the noise threshold is set to judge the noise level. If the image noise level exceeds the noise threshold, the number of filtering iterations is increased, otherwise the default parameters are used;

[0096] Right D f (x, y, z) is normalized to obtain D n (x, y, z), D n (x, y, z) represents the normalized image data, with intensity values ​​ranging from 0 to 1. min and D max Represents the minimum and maximum intensity values ​​of the image data after noise reduction; if D min =D max , if the image intensity distribution is abnormal, adjust the noise reduction parameters;

[0097] Use histogram equalization to enhance the contrast and get D e (x,y,z),D e (x, y, z) = HistEqual[Dn (x, y, z)], D e (x, y, z) represents the image data after contrast enhancement, HistEqual represents the histogram equalization operator;

[0098] The preprocessed image data D e (x, y, z) for feature extraction, specifically:

[0099] Calculate the grayscale histogram H(g), mean μ and variance σ of the image 2 Statistical features,

[0100] Where g represents the gray level, δ represents the Kronecker delta function, μ represents the mean, N represents the total number of voxels, and σ 2 represents variance;

[0101] The Sobel operator is used to calculate the gradient map G(x, y, z) of the image to obtain edge information. The gradient calculation is:

[0102]

[0103] G x =D e (x+1, y, z)-D e (x-1, y, z),

[0104] G y =D e (x,y+1,z)-D e (x, y-1, z),

[0105] G z =D e (x,y,z+1)-D e (x, y, z-1),

[0106] Set the gradient threshold. If G(x, y, z) is greater than or equal to the gradient threshold, the point is considered an edge point, otherwise, it is a non-edge point.

[0107] The gray-level co-occurrence matrix (GLCM) P(i, j, d, θ) is used to extract texture features, including energy, contrast, entropy, and inverse difference, and texture features are used to distinguish different tissue structures. Contrast entropy Reverse Gap

[0108] According to the statistical characteristics, texture characteristics and edge information of the image, an intensity threshold T is set for each tissue structure. imin and Timax And the texture feature threshold T ti , then determine the region R where the key organizational structure is located i , R i ={(x, y, z)D e (x,y,z)∈[T imin , T imax ], T(x, y, z) ≥ T ti}, T(x, y, z) represents the texture feature value calculated at the voxel (x, y, z), T ti Represents the texture feature threshold for tissue structure i; wherein the key tissue structure list: uterus (U): including uterine wall and uterine cavity, placenta (P): positioning and range, fetal head (F): position and orientation, bladder (B): to avoid damage during surgery, abdominal wall layer (A): skin, subcutaneous tissue, fascia and muscle; defines spatial position constraints: the uterus (U) should be located in the pelvic cavity and contain the placenta (P) and fetal head (F), and the bladder (B) should be located in front and below the uterus (U);

[0109] Perform nonlinear enhancement on the gradient map G(x, y, z), edge enhancement function G e (x, y, z) = G(x, y, z) γ , where γ represents the enhancement coefficient, combined with the determined key tissue structure region R i and enhanced edge information G e (x, y, z) determines the region of interest ROI i , ROI i ={(x,y,z)(x,y,z)∈R i And G e (x, y, z) ≥ T e}, where T e Represents the edge strength threshold.

[0110] The embodiment of the present invention uses an anisotropic diffusion filter to remove noise, which significantly improves the quality of the image, reduces noise interference in the image, and is helpful for subsequent feature extraction and segmentation; intensity normalization and histogram equalization further improve the contrast of the image, make the key tissue structure clearer, and ensure that the key parts such as the placenta and uterus can be accurately identified and marked in complex images; grayscale histogram and statistical feature extraction can provide a quantitative description of the overall brightness and texture, which helps the subsequent segmentation algorithm to perform preliminary classification when identifying different tissue structures; gradient maps and texture features play an important role in edge detection, making the boundaries of key tissue structures more prominent and providing accurate edge information for segmentation; by combining grayscale and texture features with tissue structure position constraints, the specific position and boundary of each key tissue can be more accurately located, ensuring that the segmentation result can clearly reflect the spatial relationship between different tissue structures, and improving the accuracy and rationality of subsequent three-dimensional modeling; the gradient map is processed by a nonlinear edge enhancement function, which further enhances the boundary information of the key tissue structure, making the segmentation algorithm more accurate in boundary judgment.

[0111] In a preferred embodiment of the present invention, S2 specifically includes:

[0112] Perform initial segmentation using the determined region of interest as the initial condition to generate a preliminary segmentation mask Preliminary segmentation mask The formula for generating is

[0113] According to the characteristics of the tissue structure, the level set method is used for fine segmentation, and the level set function is defined and initialized as: ε represents a positive number, defining the energy function Ω represents the image space, μ represents the smoothness term weight, and λ i represents the weight of the regional item, c i Represents the average gray value of the target area, represents the Heaviside function, the level set evolution equation represents the Dirac function;

[0114] Iterate the solution until convergence to obtain accurate tissue structure segmentation results;

[0115] Post-process the segmentation results, perform opening and closing operations, remove noise, smooth edges, retain the largest connected area, and remove isolated small areas;

[0116] The Marching Cubes algorithm is used to convert the segmentation mask into a 3D model of the key tissue structure, and the mesh of the model is optimized using Laplace smoothing. V i represents the vertex coordinates, Ni Represents the number of neighbor vertices, λ represents the smoothing coefficient, which reduces the number of polygons and improves rendering efficiency.

[0117] The embodiment of the present invention uses the Marching Cubes algorithm to generate a high-precision three-dimensional model by combining initial segmentation and fine segmentation, which can truly restore the three-dimensional structure of key tissues in the pelvis and abdomen of pregnant women. The accuracy and completeness of the model improve the authenticity of the virtual scene during the training process, allowing trainees to intuitively and accurately observe the spatial relationship of various anatomical structures in simulated surgery; in addition, model optimization further improves rendering efficiency, ensuring smooth visual effects and interactive experience in a mixed reality environment.

[0118] In a preferred embodiment of the present invention, S3, calculating a reference evaluation value of cesarean section training based on the characteristic values ​​of key tissue structures, and generating key indicators of cesarean section training, specifically including:

[0119] Extraction of anatomical feature parameters, including: Calculation of the volume of key tissue structures ΔV=Δx·Δy·Δz, Vi represents the volume of key tissue structure i, M i (x, y, z) represents the segmentation mask, ΔV represents the volume of a single voxel, Δx, Δy, and Δz represent the voxel spacing of the image in the x, y, and z directions; calculate the surface area of ​​key tissue structures S i represents the surface area of ​​key organizational structure i, A k represents the area of ​​the kth triangle, v 1 、v 2 and v 3 Represents the coordinates of the three vertices of the triangular patch, × represents the vector cross product, and ||·|| represents the vector modulus; calculates the centroid position of the key organizational structure (x i ,y i ,z i ) represents the centroid coordinates of the weave structure i; extract and evaluate the compactness of the key weave structure C i represents the compactness of the organizational structure i, V i Indicates volume, S i Represent surface area; and calculate distances and relative positions between tissues;

[0120] Calculate the incision position P i =P u +R θ ·d, P i Indicates the recommended incision position, P u represents the center of mass of the uterus, R θrepresents the rotation matrix constructed based on the fetal head orientation angle θ, d represents the preset incision offset vector, and d y It represents the offset distance of the incision relative to the center of mass of the uterus in the y direction;

[0121] Calculate the reference value F of the operating force r =k t ·T l , F r Indicates the reference operating force, k t Indicates the tissue hardness coefficient, T l represents the total thickness of the abdominal wall layer;

[0122] Set surgical time reference values ​​according to standard surgical procedures;

[0123] Set key indicators for surgical training based on clinical experience and teaching requirements: allowable value of incision position error ΔP max , i.e. the maximum permissible value of the cut position error; operating force range F min =F r -δF,F max =F r +δF, δF indicates the allowable deviation of force; the operation time limit is T max =T r +δT, δT represents the allowable time deviation.

[0124] The embodiments of the present invention can accurately reflect the spatial characteristics and morphological features of key tissues by extracting anatomical parameters such as volume, surface area, center of mass position and compactness, and provide a reliable basis for tissue identification and positioning in training. These characteristic parameters help to improve the simulation of the training model, so that trainees can operate in anatomical structures close to the real ones; the incision position is calculated according to the orientation of the fetal head and the position of the uterus, and the operation force is set in combination with the tissue hardness and the abdominal wall thickness, which can effectively help trainees master the appropriate incision position and force control in training, and reasonable reference value and range setting reduce the risk of misoperation; setting reasonable reference values ​​and limits for surgical time not only standardizes the trainees' operating procedures, but also helps them gradually improve their operating efficiency and improve the fluency of surgical operations.

[0125] In a preferred embodiment of the present invention, S4 specifically includes:

[0126] The constructed 3D model is converted from the original format to the target format required by the mixed reality system MR, and the coordinate system of the 3D model is converted to the coordinate system used by the MR system; it should be noted that if the model coordinate system is consistent with the MR system, no conversion is required, if not, coordinate system conversion is required, and the coordinate system conversion formula V c =R c ·V o +Τo , V o Represents the original model vertex coordinate vector, V c Represents the coordinate vector of the model vertex after transformation, R c represents the coordinate system rotation matrix, Τ o Represents the coordinate system translation vector;

[0127] Build a virtual operating room scene in the MR system, including the following elements: operating table: set the size and position of the operating table; medical instruments: surgical instrument models; lighting settings: configure ambient light and spotlight sources; simulate the lighting conditions of the operating room; other scene elements: walls and floors;

[0128] Importing the converted 3D model into the virtual scene of the MR system, and adjusting the position, rotation, and scaling parameters of the model to match the virtual operating room environment;

[0129] Designing a user interaction interface in the MR system, including operation instructions, information display and feedback prompts. The operation instructions are used to display surgical steps and precautions; the information display is used to display the trainee's operation data and evaluation results in real time; the feedback prompts are used to provide visual, auditory or tactile feedback to indicate operation errors or successes;

[0130] An interaction mode is set in the MR system, including gesture recognition, voice control and handle operation, wherein the gesture recognition is used to support the movement and rotation of the gesture control model, the voice control is used to interact with the system through voice commands, and the handle operation is used to interact using a handle or controller;

[0131] Setting rendering effects in the MR system, including material properties, lighting effects, shadows and reflections, and texture maps, wherein the material properties are used to set the reflectivity and transparency material parameters of the model, the lighting effects are used to adjust the intensity, color, and position of the light source, the shadows and reflections are used to enable shadow and reflection effects, and the texture maps are used to apply high-resolution texture maps;

[0132] Define the real space coordinate system: a coordinate system based on the real world, in which all positions and postures in the real environment are expressed; define the virtual space coordinate system: a coordinate system of the virtual model in the MR system;

[0133] Select a set of corresponding landmark points in the real space and virtual space respectively, and establish a real space landmark point set {P i r}, i = 1, 2, ..., N, establish a virtual space landmark set {P i v}, i = 1, 2, ..., N, and use the least squares method to calculate the rigid body transformation matrix T = {R, Τ}, so that Calculate the centroid of virtual and real landmarks Decentralized Coordinates Calculate the covariance matrix Perform singular value decomposition of the covariance matrix H H = UΣV Τ , calculate the rotation matrix R = VU Τ , calculate the translation vector V a =R·V v +Τ, calculate the root mean square error Among them, P i r represents the coordinates of the i-th landmark point in the real space, P i v represents the coordinates of the corresponding i-th marker point in the virtual space, N represents the number of marker points, U, Σ and V represent the SVD decomposition results of the covariance matrix, R represents the rotation matrix, Τ represents the translation vector, V v Represents the vertex coordinate matrix of the virtual model, V a represents the vertex coordinate matrix after registration, ||·|| represents the Euclidean distance, and sets the registration error threshold. If the root mean square error is less than the registration error threshold, the registration is considered successful; otherwise, re-registration is required.

[0134] The embodiment of the present invention creates a highly simulated virtual operating room scene in the MR system, so that trainees can be trained in an environment close to the real one, which increases the immersion and participation of the training. The setting of elements such as the operating table, medical equipment and lighting ensures the integrity and authenticity of the scene, which helps trainees adapt to the actual surgical environment. Through the design of gesture recognition, voice control and handle operation, trainees can naturally interact with the virtual environment and obtain real-time operation feedback. Adding rendering effects such as materials, lighting, and shadows to the virtual surgical scene makes the model and environment more realistic. The fine rendering effect enhances the visual expression of the training system and increases the immersive experience. Through the least squares method, spatial alignment is performed to achieve position matching between the virtual operating room and the real operating room. Spatial alignment ensures the synchronization of coordinates of the virtual and real spaces, so that when the trainees operate in the real space, the movement and position changes of the virtual objects can be accurately reflected in the MR system.

[0135] In a preferred embodiment of the present invention, S5 specifically includes:

[0136] Configure sensor equipment to collect real-time operation data such as the position and force of the trainee during the surgical training process. The sensor equipment includes a position sensor and a force sensor. The position sensor is used to capture the position and posture of the trainee's hand and surgical instrument in real time, and the force sensor is used to measure the force applied by the trainee during the operation.

[0137] Receive the training start signal, pre-process the collected operation data, and extract key features;

[0138] The trainee's real-time operation data is compared with the reference evaluation value, and a real-time feedback prompt is generated. The position deviation warning threshold is set and set to 80% of the maximum allowable value. When the position deviation is judged to exceed the position deviation warning threshold, a warning is triggered, a warning message is displayed on the interface, a prompt sound is played, and a warning event is recorded; the force deviation warning threshold is set and set to 80% of the maximum allowable value. When the force deviation is judged to exceed the force deviation warning threshold, a warning is triggered, a warning message is displayed on the interface, a prompt sound is played, and a warning event is recorded;

[0139] Receive the training end signal, calculate and display the trainee's total surgical training score S on the interface 总 , the total score of the surgical training is S 总 The calculation formula is:

[0140] S 总 =w 1 S p +w 2 S f +w 3 S t , w 1 +w 2 +w 3 =1,

[0141]

[0142] Among them, w 1 、w 2 and w 3 represents the weight coefficient, S p Indicates the incision location score, S f Indicates the operation strength score, S t represents the surgical time score, ΔP represents the Euclidean distance between the trainee’s actual incision position and the reference incision position, δF represents the force tolerance, and F a It represents the average force applied by the trainee during the operation, F r Indicates the reference strength, T u represents the actual time taken by the trainee to complete the operation, δT represents the time tolerance, and T r Indicates reference time;

[0143] Set the passing score threshold S 1 , and set the excellent score threshold S 2 , if S 总 >S 2 The evaluation is "excellent", if S 1 ≤S 总 <S 2The evaluation is "qualified", if S 总 <S 1 It is evaluated as “unqualified”;

[0144] Generate and display evaluation results.

[0145] The embodiment of the present invention monitors the trainee's operating behavior in real time through position sensors and force sensors, ensuring the accurate collection of hand, surgical instrument position and operating force data, and preprocessing improves the accuracy of real-time feedback; by comparing the operating data with the reference value, the system can provide instant feedback on position and force deviations, which helps the trainees to adjust the operation in time during the training process to avoid error accumulation. Real-time feedback greatly improves the effectiveness of training and enables trainees to master standardized operating methods more quickly; the scoring system for incision position, operating force and operation time can comprehensively evaluate the quality of surgical operations and help trainees understand the advantages and disadvantages of operations. The evaluation mechanism based on the scoring formula provides objective operation evaluation; the recording of warning events and interface prompts enable trainees to review specific operation deviations after training, further improving the training effect.

[0146] In a preferred embodiment of the present invention, S6 specifically includes:

[0147] Record the trainee's hand or instrument position trajectory throughout the training process, record the change of the trainee's operating force over time, record the time of each data point, and save the data in a structured format;

[0148] Receive the training end signal, compare the trainee's operation data with key indicators to analyze the cause of the error;

[0149] Classify the types of operation errors made by trainees during the training process, including positioning deviation errors where the position deviation exceeds the position deviation warning threshold, force control errors where the force deviation exceeds the force deviation warning threshold, operation process errors where the surgical steps are wrong, and time errors that exceed the time threshold, and calculate the number of different types of operation errors made by trainees during the training process;

[0150] Provides a positioning practice module for positioning deviation errors, including positioning tasks of different difficulty levels;

[0151] Provides a strength training module to address strength control errors, including simulated tissue training tasks of different hardness;

[0152] Provide video modules for operation process errors and time errors, including learning tasks of standard cesarean section operation process;

[0153] According to the ID file established for the trainee, the total score of each surgical training of the corresponding trainee is used as a data point to update and generate the trainee's score curve.

[0154] The embodiment of the present invention records the hand or instrument position, force change and timestamp in real time, provides detailed process data for the trainee's operating behavior, and classifies different types of operating deviations, which can help the system accurately identify the trainee's deficiencies and provide personalized improvement suggestions. It provides corresponding training modules and learning videos according to different types of errors, which can help trainees improve their operating skills in a targeted manner, generate a score curve for the trainees, and intuitively display the improvement of the operating level, which can help trainees and instructors better understand their operating progress trends.

[0155] In a preferred embodiment of the present invention, the method further comprises:

[0156] Set an initial warning threshold D for position deviation at the beginning of training p0 and the initial warning threshold D of the operating force f0 ;

[0157] According to the total training time T t The training process is divided into multiple time periods, each of which is T long. s ,At the end of each time period, the trainee's operation compliance rate in that time period is judged. If the operation compliance rate reaches the preset standard, the warning threshold is updated and the next time period is entered;

[0158] Update the warning threshold using a dynamic warning threshold update formula;

[0159] In each time period, the trainee's operation deviation is detected in real time. If the position force deviation exceeds the position deviation warning threshold in the current time period, D p (t), or when the force deviation exceeds the force deviation warning threshold in the current time period D f (t), immediately give a feedback tone, highlight the specific value of the deviation on the user interface, and suggest trainees to adjust their operations. If the deviation exceeds the threshold more than 3 times within a certain time period, the system will record the time period as not meeting the standard;

[0160] If N consecutive 1 The operation compliance rate in the time period is higher than the first preset compliance rate Q 1 , then the threshold attenuation rate constant in the dynamic warning threshold update formula is adjusted, that is, λ and γ are increased by 20%. 2 The operation compliance rate in the time period is lower than Q 2 , then relax the threshold adjustment rate, that is, reduce λ and γ by 10% respectively, where N 1 >N 2 And Q 1 >Q 2 ;

[0161] In the total training time T t When λ and γ are 80% of their initial values, they are reduced to 50% of their initial values ​​respectively;

[0162] At the end of the training, the system generates an operational stability report based on the deviation records of the entire training process, and statistics the average, standard deviation and compliance rate of position and force deviations.

[0163] The dynamic warning threshold update formula includes a dynamic update formula for a position deviation threshold and a dynamic update formula for a force deviation threshold, wherein:

[0164] The dynamic update formula of the position deviation threshold is:

[0165] The dynamic update formula of the force deviation threshold is:

[0166] Among them, D p (t) represents the position deviation warning threshold at time t, D f (t) represents the force deviation warning threshold at time t, D p0 Indicates the initial position deviation warning threshold, D f0 represents the force deviation warning threshold, k and m represent the dynamic adjustment coefficients of position and force feedback, T t Represents the total length of training time, T s represents the duration of a single time segment, and λ and γ represent the decay rate constants of position and strength.

[0167] The embodiment of the present invention makes staged adjustments so that trainees can gradually adapt to the training standards of each stage, improves the gradualness of skill improvement, and enhances the scientificity and sustainability of the training process. The dynamic update formula of sine and cosine functions and time decay is adopted to gradually reduce the feedback threshold during the training process, so that the operation standard becomes more stringent over time. The formula is reasonably designed to change the form to avoid the sudden change of the threshold, so that the training process is smoother and more natural, ensuring that the trainees can gradually improve the operation accuracy and force control ability in a stable environment; the tightening speed of the threshold is adaptively adjusted according to the trainee's compliance with the standard in multiple time periods, so that the training process is more flexible and personalized. If the trainee performs well, the tightening speed is accelerated, otherwise the tightening speed is slowed down to avoid excessively aggressive or too loose training. The adaptive adjustment improves the adaptability of training feedback, so that the system can flexibly respond to the trainee's operation level; at the end of the training, the decay rate of the threshold is slowed down, so that the feedback threshold tends to be stable, ensuring that the trainee can perform stable operations under high-precision conditions. This design helps the trainee to achieve the final surgical accuracy standard in a high-demand operating environment.

[0168] The embodiments of the present invention further provide device embodiments for implementing the steps and methods in the above method embodiments.

[0169] Please refer to Figure 2 , which is a functional block diagram of a cesarean section training system based on mixed reality provided by an embodiment of the present invention. Figure 2 As shown, the system includes:

[0170] An image data acquisition module, used to acquire medical image data of the pelvis and abdomen of a pregnant woman;

[0171] A preprocessing module, used for preprocessing the acquired medical image data;

[0172] A key organizational structure extraction module is used to extract the characteristic values ​​of the key organizational structure, identify and mark the boundaries and regions of the key organizational structure;

[0173] A 3D model building module is used to initially segment key tissue structures from image data, then perform fine segmentation, and use a reconstruction algorithm to build a 3D model of the key tissue structure;

[0174] An indicator generation module, used for calculating a reference evaluation value of cesarean section training based on characteristic values ​​of key tissue structures, and generating key indicators of cesarean section training;

[0175] A mixed reality conversion module is used to convert the constructed 3D model into a format recognizable by the mixed reality system so as to import it into the MR system, and perform scene setting and spatial registration;

[0176] The training analysis module is used to receive a training start signal, detect the trainee's surgical operation to collect action data, compare the trainee's operation data with key indicators, generate real-time feedback prompts, receive a training end signal, and generate evaluation results;

[0177] The evaluation feedback module is used to record operations that do not meet the evaluation standards, provide targeted improvement suggestions to trainees, and update and generate the trainees' score curve.

[0178] The cesarean section surgery training system based on mixed reality of the present invention provides an efficient, accurate and immersive surgery training platform through precise image data processing, three-dimensional modeling, real-time feedback and personalized evaluation and other technical means. The system can not only significantly improve the safety and effectiveness of surgical training, but also provide personalized guidance during the training process, helping trainees quickly master surgical skills and improve their actual surgical operation capabilities.

[0179] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments are contemplated within the scope of the invention thus described. The disclosure of the present invention is intended to be illustrative rather than restrictive of the scope of the invention, which is defined by the appended claims.

Claims

1. A cesarean section training method based on mixed reality, characterized in that: The method comprises: S1. Obtain medical imaging data of the pelvis and abdomen of pregnant women, extract characteristic values ​​of key tissue structures after preprocessing, and identify and mark the boundaries and regions of key tissue structures; S2, initially segmenting the key tissue structure from the image data, then performing fine segmentation, and using a reconstruction algorithm to construct a three-dimensional model of the key tissue structure; S3. Calculate the reference evaluation value of cesarean section training based on the characteristic values ​​of the key tissue structures, and generate key indicators of cesarean section training; S4, converting the constructed 3D model into a format recognizable by the mixed reality system to import into the MR system, and performing scene setting and spatial registration; S5, receiving a training start signal, detecting the trainee's surgical operation to collect motion data, comparing the trainee's operation data with key indicators, generating real-time feedback prompts, receiving a training end signal, and generating evaluation results; S6. Record the operations that do not meet the evaluation criteria, provide targeted improvement suggestions to trainees, and update the trainees’ score curve.

2. The method according to claim 1, characterized in that The S1 specifically includes: Obtain three-dimensional volume data D(x, y, z) of the pregnant woman's pelvis and abdomen, where x, y, and z represent spatial coordinates, and D represents the intensity value of each voxel; Apply an anisotropic diffusion filter F AD Reduce the noise to get D f (x, y, z), for D f (x, y, z) is normalized to obtain D n (x, y, z), use histogram equalization to enhance the contrast and get D e (x, y, z); The preprocessed image data D e (x, y, z) for feature extraction, specifically: calculating the grayscale histogram, mean and variance statistical features of the image, using the Sobel operator to calculate the gradient map of the image to obtain edge information, and using the grayscale co-occurrence matrix to extract texture features; Determine the area where the key tissue structure is located according to the extracted features, wherein the key tissue structure list includes: uterus (U): including uterine wall and uterine cavity, placenta (P): positioning and range, fetal head (F): position and orientation, bladder (B): to avoid damage during surgery, abdominal wall layer (A): skin, subcutaneous tissue, fascia and muscle; The gradient image is nonlinearly enhanced to enhance the edge information, and the region of interest is determined by combining the enhanced edge information and the key tissue structure region.

3. The method according to claim 2, characterized in that The S2 specifically includes: Perform initial segmentation using the determined region of interest as an initial condition to generate a preliminary segmentation mask; According to the characteristics of the tissue structure, the segmentation algorithm is selected again for segmentation, and the accurate segmentation mask is generated by iterative solution, and the segmentation result is post-processed; The Marching Cubes algorithm was used to convert the segmentation mask into a three-dimensional model of key tissue structures and perform model optimization.

4. The method according to claim 3, characterized in that The S3 specifically includes: Calculate the volume, surface area and center of mass position of key tissue structures, extract and evaluate tissue compactness, and calculate the distance and relative position between tissues; Calculate the incision position and operation force reference value, and set the operation time reference value; Generate key indicators for surgical training: allowable incision position error, operating force range and surgical time limit.

5. The method according to claim 2, characterized in that: The S4 specifically includes: Convert the constructed three-dimensional model from the original format to the target format required by the mixed reality system MR, and convert the coordinate system of the three-dimensional model to the coordinate system used by the MR system; Build a virtual operating room scene in the MR system, including the following elements: operating table: set the size and position of the operating table; medical instruments: surgical instrument models; lighting settings: configure ambient light and spotlight sources; simulate the lighting conditions of the operating room; other scene elements: walls and floors; Importing the converted 3D model into the virtual scene of the MR system, and adjusting the position, rotation, and scaling parameters of the model to match the virtual operating room environment; Designing a user interaction interface in the MR system, including operation instructions, information display and feedback prompts. The operation instructions are used to display surgical steps and precautions; the information display is used to display the trainee's operation data and evaluation results in real time; the feedback prompts are used to provide visual, auditory or tactile feedback to indicate operation errors or successes; An interaction mode is set in the MR system, including gesture recognition, voice control and handle operation, wherein the gesture recognition is used to support the movement and rotation of the gesture control model, the voice control is used to interact with the system through voice commands, and the handle operation is used to interact using a handle or controller; Setting rendering effects in the MR system, including material properties, lighting effects, shadows and reflections, and texture maps, wherein the material properties are used to set the reflectivity and transparency material parameters of the model, the lighting effects are used to adjust the intensity, color, and position of the light source, the shadows and reflections are used to enable shadow and reflection effects, and the texture maps are used to apply high-resolution texture maps; Define the real space coordinate system: a coordinate system based on the real world, in which all positions and postures in the real environment are expressed; define the virtual space coordinate system: a coordinate system of the virtual model in the MR system; Select a set of corresponding landmark points in the real space and virtual space respectively, and establish a real space landmark point set {P i r }, i = 1, 2, ..., N, establish a virtual space landmark set {P i v }, i = 1, 2, ..., N, and use the least squares method to calculate the rigid body transformation matrix T = {R, Τ}, so that P i r ≈R·P i v + Τ, Calculate the centroid of virtual and real landmarks Decentralized Coordinates Calculate the covariance matrix Perform singular value decomposition of the covariance matrix H H = UΣV Τ , calculate the rotation matrix R = VU Τ , calculate the translation vector V a =R·V v +Τ, calculate the root mean square error Among them, P i r represents the coordinates of the i-th landmark point in the real space, P i v represents the coordinates of the corresponding i-th marker point in the virtual space, N represents the number of marker points, U, Σ and V represent the SVD decomposition results of the covariance matrix, R represents the rotation matrix, Τ represents the translation vector, V v Represents the vertex coordinate matrix of the virtual model, V a represents the vertex coordinate matrix after registration, ||·|| represents the Euclidean distance, and sets the registration error threshold. If the root mean square error is less than the registration error threshold, the registration is considered successful; otherwise, re-registration is required.

6. The method according to claim 3, characterized in that The S5 specifically includes: Configure sensor equipment to collect real-time operation data such as the position and force of the trainee during the surgical training process. The sensor equipment includes a position sensor and a force sensor. The position sensor is used to capture the position and posture of the trainee's hand and surgical instrument in real time, and the force sensor is used to measure the force applied by the trainee during the operation. Receive the training start signal, pre-process the collected operation data, and extract key features; The trainee's real-time operation data is compared with the reference evaluation value, and a real-time feedback prompt is generated. The position deviation warning threshold is set and set to 80% of the maximum allowable value. When the position deviation is judged to exceed the position deviation warning threshold, a warning is triggered, a warning message is displayed on the interface, a prompt sound is played, and a warning event is recorded; the force deviation warning threshold is set and set to 80% of the maximum allowable value. When the force deviation is judged to exceed the force deviation warning threshold, a warning is triggered, a warning message is displayed on the interface, a prompt sound is played, and a warning event is recorded; Receive the training end signal, calculate and display the trainee's total surgical training score S on the interface 总 , the total score of the surgical training is S 总 The calculation formula is: <h2 style=";text-align:left;direction:ltr">S<h2 style=";text-align:left;direction:ltr"> 总 <h2 style=";text-align:left;direction:ltr"> =w1S<h2 style=";text-align:left;direction:ltr"> p <h2 style=";text-align:left;direction:ltr"> +w2S<h2 style=";text-align:left;direction:ltr"> f <h2 style=";text-align:left;direction:ltr"> +w3S<h2 style=";text-align:left;direction:ltr"> t <h2 style=";text-align:left;direction:ltr"> ,w1+w2+w3=1, Among them, w1, w2 and w3 represent weight coefficients, S p Indicates the incision location score, S f Indicates the operation strength score, S t represents the surgical time score, ΔP represents the Euclidean distance between the trainee’s actual incision position and the reference incision position, δF represents the force tolerance, and F a It represents the average force applied by the trainee during the operation, F r Indicates the reference strength, T u represents the actual time taken by the trainee to complete the operation, δT represents the time tolerance, and T r Indicates reference time; Set the qualified score threshold S1 and the excellent score threshold S2. 总 >S2, it is evaluated as "excellent", if S1≤S 总 <S2, the evaluation is "qualified", if S 总 <S1, it is evaluated as "unqualified"; Generate and display evaluation results.

7. The method according to claim 3, characterized in that The S6 specifically includes: Record the trainee's hand or instrument position trajectory throughout the training process, record the change of the trainee's operating force over time, record the time of each data point, and save the data in a structured format; Receive the training end signal, compare the trainee's operation data with key indicators to analyze the cause of the error; Classify the types of operation errors made by trainees during the training process, including positioning deviation errors where the position deviation exceeds the position deviation warning threshold, force control errors where the force deviation exceeds the force deviation warning threshold, operation process errors where the surgical steps are wrong, and time errors that exceed the time threshold, and calculate the number of different types of operation errors made by trainees during the training process; Provides a positioning practice module for positioning deviation errors, including positioning tasks of different difficulty levels; Provides a strength training module to address strength control errors, including simulated tissue training tasks of different hardness; Provide video modules for operation process errors and time errors, including learning tasks of standard cesarean section operation process; According to the ID file established for the trainee, the total score of each surgical training of the corresponding trainee is used as a data point to update and generate the trainee's score curve.

8. The method according to claim 7, characterized in that The method further comprises: Set an initial warning threshold D for position deviation at the beginning of training p0 The initial warning threshold D of the operation intensity f0 ; According to the total training time T t The training process is divided into multiple time periods, each of which is T long. s ,At the end of each time period, the trainee's operation compliance rate in that time period is judged. If the operation compliance rate reaches the preset standard, the warning threshold is updated and the next time period is entered; Update the warning threshold using a dynamic warning threshold update formula; In each time period, the trainee's operation deviation is detected in real time. If the position force deviation exceeds the position deviation warning threshold in the current time period, D p (t), or when the force deviation exceeds the force deviation warning threshold in the current time period D f (t), immediately give a feedback tone, highlight the specific value of the deviation on the user interface, and suggest trainees to adjust their operations. If the deviation exceeds the threshold more than 3 times within a certain time period, the system will record the time period as not meeting the standard; If the operation compliance rate for N1 consecutive time periods is higher than the first preset compliance rate Q1, the threshold attenuation rate constant in the dynamic warning threshold update formula is adjusted, that is, λ and γ are increased by 20%. If the operation compliance rate for N2 consecutive time periods is lower than Q2, the threshold adjustment rate is relaxed, that is, λ and γ are reduced by 10% respectively, where N1>N2 and Q1>Q2; In the total training time T t When λ and γ are 80% of their initial values, they are reduced to 50% of their initial values ​​respectively; At the end of the training, the system generates an operational stability report based on the deviation records of the entire training process, and calculates the average value, standard deviation and compliance rate of position and force deviations.

9. The method according to claim 8, characterized in that The method further includes: the dynamic warning threshold update formula includes a dynamic update formula for a position deviation threshold and a dynamic update formula for a force deviation threshold, wherein: The dynamic update formula of the position deviation threshold is: The dynamic update formula of the force deviation threshold is: Among them, D p (t) represents the position deviation warning threshold at time t, D f (t) represents the force deviation warning threshold at time t, D p0 Indicates the initial position deviation warning threshold, D f0 represents the force deviation warning threshold, k and m represent the dynamic adjustment coefficients of position and force feedback, T t Represents the total length of training time, T s represents the duration of a single time segment, and λ and γ represent the decay rate constants of position and strength.

10. A cesarean section training system based on mixed reality, characterized in that: include: An image data acquisition module, used to acquire medical image data of the pelvis and abdomen of a pregnant woman; A preprocessing module, used for preprocessing the acquired medical image data; A key organizational structure extraction module is used to extract the characteristic values ​​of the key organizational structure, identify and mark the boundaries and regions of the key organizational structure; A 3D model building module is used to initially segment key tissue structures from image data, then perform fine segmentation, and use a reconstruction algorithm to build a 3D model of the key tissue structure; An indicator generation module, used for calculating a reference evaluation value of cesarean section training based on characteristic values ​​of key tissue structures, and generating key indicators of cesarean section training; A mixed reality conversion module is used to convert the constructed 3D model into a format recognizable by the mixed reality system so as to import it into the MR system, and perform scene setting and spatial registration; The training analysis module is used to receive a training start signal, detect the trainee's surgical operation to collect action data, compare the trainee's operation data with key indicators, generate real-time feedback prompts, receive a training end signal, and generate evaluation results; The evaluation feedback module is used to record operations that do not meet the evaluation standards, provide targeted improvement suggestions to trainees, and update and generate the trainees' score curve.

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