84 results about "Computer-aided diagnosis" patented technology

The embodiment of the invention provides an image processing and computer-aided diagnosis method, electronic equipment, a storage medium and a program product, and the image processing method comprises the steps: obtaining a to-be-processed medical image which comprises the information of a plurality of target parts; performing first feature extraction on the medical image to obtain medical image features of the medical image; performing second feature extraction on the medical image features to obtain part identification information of each target part corresponding to the plurality of target parts in the medical image features; taking the part identification information of each target part as guidance, performing part feature extraction based on the medical image features, and obtaining part features of each target part; and predicting text features of the text based on the part features of the target parts and the detection results corresponding to the target parts to obtain prediction detection results of the multiple target parts. Therefore, the target part in the medical image can be accurately positioned and detected without depending on an external mask.

The invention discloses an intelligent auxiliary diagnosis and grading system for gastrointestinal tumor endoscopic images, which belongs to the technical field of medical image processing and computer-aided diagnosis and comprises a multi-modal image preprocessing and segmentation module, a lesion feature extraction and diagnosis module, a grading and depth evaluation module and a closed-loop feedback optimization module. The system receives white light, a narrow band and an amplified endoscopic image, adaptive segmentation is performed to obtain a lesion area, mucous membrane morphology, capillary and gland features are extracted, Paris classification, Vienna classification and infiltration depth evaluation are realized, and segmentation parameters are subjected to closed-loop optimization according to classification confidence. And intelligent auxiliary support is provided for early diagnosis and treatment decision of gastrointestinal tumors.

The invention provides an intelligent assessment method for cervical lateral deviation based on multi-modal fusion and deep learning, and solves the technical problems of single data source, weak feature generalization ability and large assessment result error caused by only analysis of a static single frame in existing cervical lateral deviation assessment. The method comprises the steps of obtaining multi-modal data, performing preprocessing, attitude normalization and feature weighted fusion to obtain fused multi-scale features, performing mapping to generate a heat map, and performing peak retrieval on the heat map to output key points; tracking time sequence tracks of the key points, correcting abnormal key points, and collecting continuous multi-frame key points for key point detection to obtain target key points; and constructing a cervical vertebra trunk coupling mechanical model, dynamically optimizing a gravity line reference axis, correcting pelvic inclination attitude deviation, and optimally solving the optimal offset through a Lagrange multiplication method and a least square method. The method can be widely applied to the technical field of medical image analysis and computer-aided diagnosis.

The invention relates to the field of medical image processing and computer-aided diagnosis, in particular to an OCT (optical coherence tomography) retina layer and hydrops segmentation method based on a graph attention dual-decoder network, which comprises the following steps of: preparing and preprocessing data, acquiring OCT image data and generating corresponding segmentation labels and boundary labels; feature coding: processing the input image through a shared encoder network, and extracting and outputting feature maps of multiple levels; carrying out dual-path decoding and interaction, carrying out region segmentation and boundary segmentation by using two parallel decoders, and carrying out cross-path feature interaction through an attention mechanism during decoding; a graph attention mechanism is introduced into a region segmentation path, and information modeling is carried out on the deep features; and calculating a composite loss function according to the output of the decoder, and optimizing network parameters. According to the method, high-precision segmentation of the lesion OCT image is realized through a double-decoder multi-task architecture, cross-level space attention interaction and image attention global reasoning.

This invention relates to a grading method for diabetic retinopathy based on a local-global interactive dual-branch network, belonging to the fields of deep learning, medical image analysis, and computer-aided diagnosis. It includes: inputting the generated initial feature sequence into a global scanning module; using dilated reparameterized convolution to capture multi-scale structural prior information; inputting the feature map with structural prior information into a core dual-branch LoGo module; the local flow extracting fine-grained lesion features through depthwise separable convolution; the global flow generating spatially variable convolution kernels based on the global context through contextual hybrid dynamic convolution; and performing bidirectional cross-modulation through an adaptive multi-scale feature interaction aggregation module to sharpen local details for global semantics and filter local noise from the global context, ultimately outputting a disease severity grade. This invention explicitly simulates the cognitive strategies of clinicians, significantly improving grading accuracy while maintaining computational efficiency.

This invention discloses a multi-label classification method for medical images based on spatiotemporal integration and adaptive normalization, belonging to the field of medical image processing and computer-aided diagnosis technology. The invention proposes an improved scheme. Firstly, it constructs a dynamically balanced data input stream through frequency inverse sampling and cosine annealing strategies. Secondly, it introduces group normalization to replace batch normalization in the feature extraction network, solving the training convergence problem under limited GPU memory. During training, it employs a focal loss function to mine hard-to-classify samples. In the inference stage, the invention constructs a spatiotemporal integration mechanism based on multi-view space transformation and multi-stage model weight fusion, combined with an adaptive threshold decision algorithm based on maximizing F1 scores to output diagnostic results. This invention effectively improves the recognition accuracy of key pathological features such as cardiac hypertrophy and edema, while significantly improving the recall rate of small lesions, exhibiting high robustness and clinical auxiliary value.

The application relates to a light wavelet enhancement fusion medical image segmentation method, and belongs to the technical field of medical image processing and computer-aided diagnosis. The core of the method is to construct a light WEF-Net network, which comprises an encoder, a bottleneck layer and a decoder. The encoder adopts a dual-domain perception module, extracts complementary features from the frequency domain and the time domain through wavelet transformation and convolution, and simultaneously extracts complementary features from the frequency domain and the time domain. The bottleneck layer is designed with a feature aggregation Kolmogorov-Arnold transformation module, which is used for efficiently fusing multi-scale semantic information and enhancing the nonlinear modeling capability. The network level also integrates a sawtooth rolling feature fusion module, which enhances the global continuity of the features through the channel rolling and spatial scanning mechanism to improve the integrity of the boundary segmentation. Experiments show that the method realizes excellent performance on multiple public medical image datasets, while maintaining low parameter quantity and low computational quantity, and significantly improves the segmentation precision.

The invention discloses a computer-aided diagnosis method and system for Alzheimer's disease analysis, and relates to the technical field of medical information processing, and the key points of the technical scheme are as follows: the method comprises the following steps: obtaining a T1 weighted brain magnetic resonance image of a subject; dividing the two-side sea horse tooth-shaped gyrus areas; image omics features are extracted from the subregions; selecting a group of discriminative feature combinations containing texture features from the right dentate loop and first-order statistical features from the left dentate loop through a multi-layer screening strategy; and inputting the feature combination into a pre-trained machine learning model to generate a diagnosis classification result. According to the method, high-precision and high-sensitivity diagnosis of the Alzheimer's disease is realized by using non-invasive standard MRI data and capturing the microstructure change of dentate gyrus, and the diagnosis result has strong correlation with key plasma biomarkers and cognitive level, so that the method has important clinical application value.

The invention provides a breast lump segmentation method based on channel-guided double-pooling multi-scale space attention, and aims to solve the problems of small area, low contrast, fuzzy boundary and the like of breast lumps in an X-ray image, the network extracts full-view image features and compresses space dimensions through a deep residual encoder; and realizing fusion of low-level details and high-level semantic features in the decoder by means of jump connection. A double-pooling gating mechanism is built in the decoder, channel guide weights are generated in parallel through global average pooling and maximum pooling, lesion significant features are screened in a self-adaptive mode, and redundant backgrounds are restrained; the multi-scale space attention module captures multi-scale space information through multi-branch large-kernel separable convolution, generates a space attention graph, combines the space attention graph with channel weights element by element, and accurately focuses a lesion area and a boundary. Experiments show that the Dice coefficients of the method on INbreast, CBIS-DDSM and private In-home data sets respectively reach 90.94%, 80.60% and 84.50%, the method is superior to a mainstream method, the segmentation precision and generalization ability are improved, and reliable support is provided for early screening and computer-aided diagnosis of breast cancer.

The application relates to a chronic kidney disease prediction method based on clinical information graph representation, belongs to the technical field of computer-aided diagnosis of chronic kidney disease (CKD), and aims to solve the problem of missed diagnosis caused by the fact that the kidney function index of early CKD is not obvious. The incidence of CKD is high, the early symptoms are hidden, and the disease is easy to be missed, thus developing into end-stage renal disease. Therefore, the application provides a chronic kidney disease prediction method based on clinical information graph representation. The method first extracts fundus image and clinical index features; the clinical index is fused into a clinical index joint feature through text embedding and numerical feature fusion, and the joint feature is fused with the fundus image feature through cross-modal attention; the similarity between subjects is calculated based on the fused feature, and a subject relationship graph is constructed by using an adaptive dynamic threshold mechanism; finally, a hybrid graph neural network is used for graph representation learning, pathological similarity between subjects is mined, and accurate prediction of chronic kidney disease is realized. The application can improve the detection rate of early CKD and is applied to non-invasive early screening and risk early warning.

The invention discloses a breast cancer MRI image enhancement and focus automatic identification method based on an attention mechanism, and relates to the technical field of data processing and computer-aided diagnosis. The method comprises the following steps: acquiring a multi-time-point mammary gland MRI image sample and performing preprocessing; performing random window enhancement and random channel exchangeable enhancement on the preprocessed sample, inputting the enhanced image sample into an improved convolutional neural network, and performing deep metric learning loss function optimization based on proxy NCA to extract focus embedding features; according to the focus embedding features, constructing a moment matching game framework with focus state distribution marked by experts as a target; and according to a moment matching game framework, adopting an inverse reinforcement learning strategy without reinforcement learning, carrying out iterative optimization on parameters of the lesion recognition model through a regret-moment-free matching algorithm, carrying out lesion property judgment according to the optimized model, and outputting a judgment result and a corresponding confidence score. Therefore, MRI image adaptive enhancement and accurate focus automatic identification are realized.

The embodiment of the invention provides a dynamic function network adaptive construction method for physiological signal analysis and related equipment, and belongs to the technical field of biomedical signal processing and computer-aided diagnosis. The method comprises the following steps: inputting a multi-channel physiological signal and extracting a preset frequency band; performing time sequence segmentation on the signal by adopting a sliding window; calculating a phase locking value matrix among all channels for each time window; carrying out personalized sparsification processing on each connection matrix by adopting a self-adaptive threshold value method; and constructing a dynamic graph sequence based on a sparse result, wherein node features adopt weighting centrality. According to the method, the time-varying information of the brain function network is completely reserved through the dynamic graph sequence, the noise robustness and personalized processing are realized through the adaptive threshold, the learning efficiency of the subsequent graph neural network is enhanced through the structure-related node features, the accuracy of disturbance of consciousness classification is remarkably improved, and the classification efficiency of the disturbance of consciousness is improved. And the method has the universality of expanding to other physiological signal analysis and industrial monitoring fields.

A computer-implemented diagnostic-assistance system for medical applications, comprises: an artificial intelligence neural network configured to classify images of an obtained image dataset according to a set of classes; a confidence module configured to generate a confidence measure associated with each of the classified images; a tagging module configured to generate, for the patient, a diagnostic signal based on the generated confidence measures associated with the classified images, wherein the diagnostic signal for the patient is tagged as conclusive if a processed combination of the confidence measures fulfills a condition and tagged as inconclusive if the processed combination of the confidence measures does not fulfill the condition; and an output interface configured to output the diagnostic signal, wherein if the diagnostic signal is conclusive the classification result is released, and if the diagnostic signal is inconclusive an additional diagnostic analysis is triggered.

This invention discloses a heterogeneous two-stream fusion method and system for grading diabetic retinopathy (DR), comprising obtaining the DR grading output using a heterogeneous two-stream architecture: processing the input fundus image into images of different resolutions; extracting global contextual features from the low-resolution image using a lightweight visual Transformer model distilled from composite knowledge, and extracting local lesion features from the high-resolution image using a convolutional neural network model; interactively fusing the global contextual features and local lesion features of the two-branch architecture through a symmetrical bidirectional cross-attention fusion module to obtain an enhanced fused feature representation; and finally inputting the fused features into a classifier to output the DR severity grading result. This invention aims to improve the accuracy and robustness of grading diagnosis through in-depth analysis of global information and local details, and can be applied to medical fields such as clinical computer-aided diagnosis and ocular image analysis.

The application discloses a skeleton quality driven tubular structure segmentation closed loop optimization method and system, which is applied to the technical field of biomedical image processing and computer aided diagnosis, and the method comprises the following steps: using a trained segmentation model to infer medical volume data to obtain a segmentation mask; skeletonizing the segmentation mask to extract a skeleton graph; performing topological defect detection on the skeleton graph to obtain a structured defect report; reversely mapping three-dimensional coordinates in the report back to a voxel space, taking each defect point as a center, a preset radius as a range, and generating a defect density weight graph according to a severity score; and using the weight graph as a spatial weighting parameter of a loss function to optimize the segmentation model; and the application reversely maps the topological defects detected by skeletonization into a weight graph and integrates the weight graph into a loss function for closed loop iteration, realizes directional repair on high-occurrence areas such as fractures and false branches, and improves the conduction efficiency of segmentation improvement to skeleton quality.

The application belongs to the field of intelligent medical computer-aided diagnosis application, and relates to a colon polyp segmentation method based on deep learning fusion and collaborative learning enhancement. The method comprises a feature extraction model, a fusion module and multi-view collaborative learning. The feature extraction model is divided into two branches. One branch uses DeiT-Small to extract global feature information and establish the correlation between each pixel. The other branch uses HardNet-MSEG to extract local feature information and obtain more low-level detail information. In order to improve the segmentation accuracy of small target colon polyp images, based on the public colon polyp image dataset and the initial deep learning single-branch segmentation method, a deep learning technology fusion method is proposed, and multi-view collaborative learning is used to enhance colon polyp segmentation. Compared with the feature extracted by a single deep learning method before improvement, the feature is richer, and the information omission defect of a single branch is compensated.

The invention belongs to the technical field of medical image processing, provides a medical image-based computer-aided diagnosis method and system, and solves the problem of insufficient computer-aided diagnosis. The method comprises the following steps: collecting a brain diffusion tensor image of a target object and a surface electromyogram signal of an associated muscle group; converting the image into Riemannian manifold data through tensor resolving and symmetric positive definite matrix mapping; extracting a Hurst index of the electromyographic signal based on remarking range analysis, and generating a motion feature vector; using Riemannian logarithm mapping and canonical correlation analysis to project manifold data and motion features to a correlation space, and extracting a maximum correlation component to generate a coupling feature vector; determining a reconstruction site through Riemannian index mapping, and calculating a geodesic line length between the reconstruction site and the reference state point to obtain a deviation value; and quantitatively judging the nerve remodeling degree and the motor function level of the stroke patient according to the deviation value. According to the application, accurate quantitative evaluation of the stroke nerve remodeling and motion recovery state is realized.

The invention belongs to the technical field related to computer-aided diagnosis, and discloses an establishment method of a bladder outlet obstruction auxiliary judgment model, an auxiliary judgment method, an auxiliary judgment device and an auxiliary judgment system.The establishment method comprises the steps that multiple pieces of urodynamics sample data are acquired to establish a data set; the sample data comprises multi-channel time sequence data; constructing a space-time model, and training to obtain an auxiliary judgment model; the spatial-temporal model is specifically characterized in that multi-channel time sequence data is input into a coding module for coding, multi-channel and single-channel convolution is carried out on output of the coding module through a variable channel convolution module to obtain convolution stacking feature vectors with a plurality of output channels, and then the convolution stacking feature vectors are fused through a self-attention module; and the classification network identifies and outputs a judgment result. According to the method, the spatial-temporal model is constructed, so that the coupling relationship among multiple channels and the time relationship in the channels can be effectively captured, and the judgment accuracy of an intelligent judgment model based on multi-time sequence data can be improved.

This invention discloses a computer-aided diagnostic method based on deep learning, comprising the following steps: collecting multi-source operational status data of a computer system and performing time alignment to form a system operational status data sequence; constructing a multi-scale system status sequence set; inputting the multi-scale system status sequence set into an improved TimeGPT model to generate a baseline status sequence for healthy system operation; calculating the state deviation of the system operational status data sequence and extracting the recoverability features of the deviation; performing cumulative evolution analysis across operational cycles on low-recoverability state deviations to construct anomaly evidence vectors; determining the irreversibility of risk based on the anomaly evidence vectors, generating auxiliary diagnostic results and determining the risk level; and outputting system operation early warning information based on the auxiliary diagnostic results. This invention can achieve accurate identification and risk classification of computer system operational anomalies, providing reliable support for operation and maintenance decisions and possessing significant engineering application value.

The invention discloses an interpretable MRI (Magnetic Resonance Imaging) image analysis method and system based on anatomical constrained attention, and relates to the field of medical image intelligent analysis and computer-aided diagnosis. According to the method, Hadamard product fusion is carried out on a neural image anatomical region mask and spatial attention, depth feature mining is guided by anatomical structure constraints, and the depth feature mining accuracy is improved. The attention of the model focuses on a disease specific area; a single-channel ResNet50 is adopted to adapt to MRI gray scale characteristics, and key slice dynamic screening and weighted fusion strategies are combined, so that the calculation time is shortened, and rapid and accurate research and judgment are realized; and a thermodynamic diagram is generated through fusion of multi-scale Grad-CAM and anatomical priori knowledge, so that the interpretability is enhanced. According to the invention, a high-precision and interpretable intelligent solution is provided for efficient identification and auxiliary diagnosis of NIID rare diseases and hydrocephalus.

This invention discloses a method and system for pathological classification of pulmonary nodules based on dynamic phenotypic subspace inference, belonging to the field of medical image processing and computer-aided diagnosis technology. The method includes: constructing a multi-center feature template library; extracting query features and matching them with the template library, using the maximum similarity under each category as the classification criterion; constructing a joint loss function containing cross-entropy and hierarchical loss to update the feature extractor; performing gradient-free dynamic replacement of the template library based on diversity gain; and outputting prediction results by matching with a frozen template library during testing. This invention also discloses a corresponding classification system. By constructing a dynamic multi-center feature template library, this invention explicitly accommodates intra-class phenotypic diversity. Combined with ordinal consistency constraints and a dynamic template update strategy, it can effectively learn continuous feature representations of the evolution of pathological malignancy, thereby achieving dynamic matching and accurate identification of complex pulmonary nodule phenotypes and assisting in precise clinical diagnosis and treatment.

The invention provides a method for normalizing nuclear imaging data by processing it through a deep learning algorithm, specifically a neural network comprising at least one convolutional layer, to reduce inter-center, intra-center, and patient weight biases. The method accepts data from diverse sources, including different imaging centers, scanner models, and patient populations, and applies bias reduction techniques, such as normalizing image intensity, to produce normalized data. This normalized data is then utilized in various downstream tasks, such as computer-aided diagnosis and clinical trials, yielding improved accuracy compared to non-normalized data. The neural network is designed to adapt to new data sources without retraining, enhancing the method's flexibility.

This application discloses an image processing method, a computer-aided diagnosis method for tumors, and a computing device, relating to large model technology and the field of image processing. The method includes: performing image segmentation on the image to be processed to obtain a mask image of the detection region; determining the spatial location information of the detection region in the image to be processed based on the mask image; performing denoising processing on the image to be processed based on the spatial location information to obtain an initial enhanced image; and performing detail restoration on the initial enhanced image based on a preset resolution to obtain a target enhanced image, wherein the display clarity of the detection region in the target enhanced image is higher than the display clarity of the detection region in the image to be processed. This application solves the technical problem of poor image processing performance in related technologies.

The invention provides a method, a system, equipment, a medium or a program product for computer-aided diagnosis or drug screening based on KRTDAP. The invention discovers that the expression of KRTDAP in oral submucosal fibrosis patients is obviously increased, and further research discovers that the highly expressed KRTDAP has the effect of promoting the progress of oral submucosal fibrosis, which prompts that the KRTDAP is possibly related to the diagnosis of oral submucosal fibrosis. On the basis, an efficient and rapid method is provided for diagnosis of oral submucosal fibrosis patients and screening of targeted KRTDAP drugs, and the method has important significance on prevention and treatment research of oral submucosal fibrosis.

This invention relates to the field of computer-aided diagnosis, and in particular to a breast tumor identification system based on multimodal ultrasound imaging. The breast tumor identification system provided by this invention performs the following steps: acquiring a first ultrasound modality breast image, and locating a suspected first breast tumor region in the first ultrasound modality breast image; acquiring a second ultrasound modality breast image, and aligning the second ultrasound modality breast image with the first ultrasound modality breast image; based on the alignment result, locating a second suspected breast tumor region in the second ultrasound modality breast image corresponding to the suspected first breast tumor region; and identifying the confidence level of the breast tumor in the suspected second breast tumor region based on the distribution of microvessels in the suspected second breast tumor region. The breast tumor identification system based on multimodal ultrasound imaging provided by this invention, combining breast images from both the first and second ultrasound modalities, achieves more accurate breast tumor identification and localization, improving detection accuracy.

The application discloses a kind of based on the design method of structured report template of semasiology association, comprising the following steps: step S1, based on historical medical record big data semantics construction obtains first diagnosis and treatment knowledge graph;Step S2, based on the construction of second diagnosis and treatment knowledge graph of disease diagnosis and treatment guideline big data;Step S3, the first diagnosis and treatment knowledge graph and second diagnosis and treatment knowledge graph are fused to obtain the diagnosis and treatment structured knowledge graph of fusion representation diagnosis and treatment practical experience and diagnosis and treatment expert experience, and based on diagnosis and treatment structured knowledge graph constructs structured report template for disease category;Step S4, according to structured report template, uniform diagnosis and treatment is carried out to disease category to improve diagnosis and treatment standardization.The application makes it in accordance with structured report template to carry out disease diagnosis and treatment, that is, it is in accordance with diagnosis and treatment practical experience of doctor also in accordance with diagnosis and treatment expert experience, realizes diagnosis and treatment standardization and maneuverability, breaks through the limitation that computer-aided diagnosis method only uses diagnosis and treatment guideline driving.

The application discloses a brain tumor MRI image semantic segmentation method, and belongs to the technical field of medical image processing and computer-aided diagnosis. The application solves the problem of low segmentation precision based on the existing brain tumor MRI image semantic segmentation technology. The application proposes a semantic segmentation model combining the U-Net architecture with the LWD module, the MFDF module and various filters. The LWD module can keep as much information as possible in the down-sampling process. The MFDF module constructs a new directional gradient feature extraction operator and combines different direction operators by using double-channel filtering, so that the constructed multi-direction filter can extract low-frequency and high-frequency feature direction information respectively. The MFDF module transmits the detail features from the encoding module to the corresponding decoding module, thereby recovering the spatial information including the feature boundary and texture and improving the segmentation precision of the model. The application has good adaptability to the randomness of the shape, size and boundary of the brain tumor. The method can be applied to brain tumor MRI image segmentation.

The invention relates to the technical field of computer-aided diagnosis, in particular to a double-view autism detection method and system based on asynchronous brain function prior. The method comprises the following steps: firstly, acquiring an original fNIRS blood oxygen data sequence and double-view video data of a subject during an experimental task; constructing a global neural encoder by adopting a VGG-like depth one-dimensional convolution architecture, and extracting spatial-temporal features of brain functions to generate a global neural feature vector; constructing a cross-modal channel attention generation network, and generating a channel attention weight vector; injecting the channel attention weight vector into a self-attention module for video feature extraction, and performing channel-level dynamic calibration; and calculating a differential representation vector by adopting a double-flow feature alignment module of time delay perception, and outputting a prediction result of the autism spectrum disorder. According to the method, on the premise that strict time synchronization is not needed, random action noise irrelevant to pathology in the video can be dynamically inhibited, and behavior defect characteristics relevant to neural abnormality can be amplified.

The present application relates to the technical field of medical image processing and computer-aided diagnosis, and particularly relates to a multi-modal magnetic resonance image segmentation method based on MSBA-Net, aiming at significantly reducing the model parameter quantity and improving the training and inference efficiency under the premise of ensuring the segmentation accuracy. The method comprises the following steps: acquiring multi-modal brain tumor magnetic resonance images and preprocessing to obtain preprocessed images; inputting the preprocessed images into a compact 3D segmentation network MSBA-Net to extract multi-scale deep semantic features; based on the multi-scale deep semantic features, outputting a preliminary segmentation prediction map through a decoder, and outputting an auxiliary segmentation prediction map at four resolution levels by using a deep supervision mechanism; constructing a boundary-guided hybrid loss function, training the compact 3D segmentation network MSBA-Net by using the boundary-guided hybrid loss function, and segmenting the test images in the test stage to output the segmentation masks of the whole tumor region, the tumor core region and the enhanced tumor region of the brain tumor.

The application relates to the technical field of medical image processing and computer-aided diagnosis, in particular to an orthopedic auxiliary examination system based on image processing; the system comprises a tensor field mapping, a texture flow construction, diffusion enhancement, singularity analysis and a quantization unit; the system uses a structure tensor to construct a texture flow field to suppress background noise and metal artifacts; the core is to use a diffusion equation evolution flow field to amplify texture dislocation, identify topological singular points serving as fracture endpoints by calculating Poincare indexes, and reconstruct a lesion boundary by using a geodesic algorithm; the application solves the problem of microtexture loss under low bone density or metal interference, and realizes high-signal-to-noise ratio lesion accurate positioning and healing quantization.