30 results about "Clinical imaging" patented technology

The invention discloses a hepatocellular carcinoma postoperative early recurrence prediction method based on multi-modal fusion. The method comprises the following steps: firstly, integrating clinical data of a training set, a preoperative enhanced CT image and a postoperative full-view digital pathological image, and carrying out standardized correction; then, traditional image omics features and deep learning features are extracted from the CT image, cell nucleus morphological features and tumor microenvironment spatial configuration features are extracted from the pathological image, and key feature signatures are screened out through a maximum correlation minimum redundancy algorithm (mRMR) and LASSO regression in combination with clinical features. And then carrying out progressive model construction by adopting an XGBoost algorithm, sequentially establishing a clinical single-mode model, an image single-mode model, a pathological single-mode model and a multi-mode fusion model, and explaining and visualizing the models by utilizing an SHAP value and a Grad-CAM technology. Finally, the performance of the model is evaluated in a multi-dimensional mode through internal cross validation, foresight and external independent validation, risk layering is carried out based on the prediction probability, and individualized postoperative management is guided.

The invention discloses a model-based cardiovascular image analysis, comparison and recognition system, which relates to the technical field of intelligent diagnosis and comprises an artifact preliminary detection module, an artifact recognition module, a boundary reconstruction module, an authenticity verification module, an interference learning module and a closed-loop regulation and control module, based on frequency domain analysis and spatial feature deconstruction, a multi-scale artifact analysis model is constructed, and preliminary positioning and signal separation of an artifact region are realized; and the artifact identification module is used for executing gray gradient aggregation and structural symmetry comparison based on a positioning result of the artifact analysis model, and generating an artifact mode label containing interference intensity, spatial distribution and morphological difference information. According to the invention, accurate separation of artifacts and real tissues and dual verification of lesions are realized through cooperation of multiple modules, a feedback optimization mechanism is constructed to improve recognition stability, misdiagnosis and excessive intervention risks are effectively reduced, and intelligent recognition capability and application security in clinical images are enhanced.

The invention discloses a liver cancer prognosis risk assessment method and system based on data analysis, and relates to the technical field of intelligent medical treatment. The method is realized through the following steps: firstly, acquiring and standardizing clinical, image, genome and other multi-modal data of a patient; then constructing an individualized dynamic causal graph based on the medical knowledge base, and dynamically adjusting a causal edge weight by using a graph attention network; inputting the causal diagram into a prognosis evaluation model to obtain a basic risk trajectory, performing intervention simulation by modifying a treatment node state, and calculating an anti-factual risk trajectory to quantify an individual treatment effect; and finally, outputting a risk assessment result and generating natural language interpretation containing the quantitative contribution degree. The corresponding system comprises a data sensing layer, a causal calculation layer, an intelligent decision-making layer and an interactive presentation layer. According to the method, causal reasoning and deep learning are creatively combined, and dynamic, accurate and interpretable prognosis evaluation and intervention guidance of the liver cancer are realized.

The invention discloses a method for establishing a column diagram model for diagnosis and prediction of different types of temporal lobe epilepsy. The method comprises the following steps: acquiring clinical data and an MRI image of a temporal lobe epilepsy patient; constructing a clinical feature prediction model by utilizing Logistic regression; preprocessing the MRI image, segmenting a hippocampus region of interest (ROI) and extracting radiomics features, establishing a Radscore model based on screened image features, and combining the Radscore model with clinical features to construct a joint model; performing performance evaluation on the clinical model, the radiomics model and the joint model; and constructing a temporal lobe epilepsy typing prediction column diagram based on the joint model, and realizing prediction result visualization and individualized risk assessment. According to the method, the clinical-image combined model is developed by integrating high-throughput radiomics characteristics and clinical independent factors, high-precision prediction of the temporal lobe epilepsy type is achieved, and the method can provide a reliable basis for clinical early-stage typing diagnosis, precise intervention and personalized treatment.

The invention belongs to the field of medical image processing, particularly relates to a general medical image segmentation method and system based on semantic aggregation, and aims to solve the problem that an existing model is poor in generalization ability when facing a new data field. The core of the method is that an expectation maximization semantic aggregation mechanism is adopted, and iterative purification is performed on visual features extracted from an image and language features extracted from a text so as to enhance semantic compactness of the features and form stable text semantic anchor points; then, through a depth alignment strategy, carrying out cross-attention fusion on the text semantic anchor point and the most abstract feature of the visual network; and finally, accurately guiding pixel-level segmentation decoding by using the fused features. According to the method, the segmentation performance and robustness of the model on unseen data are remarkably improved by forcing the model to learn an invariant internal semantic structure in the field, and the method can be widely applied to multi-center and cross-equipment clinical image intelligent analysis.

The application discloses a clinical image lesion classification method based on adaptive frequency domain learning and anti-aliasing complex convolution, comprising: carrying out pretreatment and normalization treatment on an original clinical image to obtain a time domain image; converting the time domain image into a frequency domain image through a position consistency module to generate a frequency spectrum capable of preserving local and position information; carrying out masking and supervision on the generated frequency spectrum through a high-frequency self-encoder module to obtain a self-encoding frequency spectrum; inputting the self-encoding frequency spectrum into an anti-aliasing complex convolution model for training to learn specific features of different clinical lesions in the image; and integrating and classifying the specific features learned by the anti-aliasing complex convolution model to obtain a clinical image lesion classification result. The method can better utilize frequency domain information of the image on the basis of preserving time domain features of the clinical image, solves the problems of brightness imbalance and inter-class confusion in lesion classification, and greatly improves the accuracy of automatic classification.

The invention relates to the technical field of medical artificial intelligence prediction, and discloses a method for predicting the total lifetime of bladder cancer through a multi-modal fusion deep learning model. The method includes quality control and normalized preprocessing by collecting clinical, image and genetic data of a patient. Then multi-modal features are extracted, and high-value features are screened out through feature importance evaluation to form an enhanced feature set; a feature interaction network is adopted to learn a complex dependency relationship among different modal features, and deep fusion is realized to generate integrated feature representation. And finally, constructing a depth prediction model based on the representation, and capturing a mapping relation between the depth prediction model and the total lifetime. Through the deep fusion and feature screening technology, multi-modal information is integrated, the defect that a traditional method neglects interaction between modals is overcome, and the accuracy and reliability of predicting the total lifetime of the bladder cancer patient are improved.

This invention discloses a method and system for pontine infarct segmentation and END prediction based on multimodal joint learning. The method includes acquiring and preprocessing multimodal data; constructing a wavelet transform-based feature encoding network to perform multi-scale decomposition and detail preservation of features; constructing a dual-task guided fusion module to align the deep semantics of clinical variables and imaging features and generate task-specific representations; constructing a Mamba-based global feature aggregation module to model sequence dependencies using a state-space model; constructing a multimodal second-order fusion classifier to enhance the clinical-image interaction modeling using second-order statistics; and employing a two-stage joint training strategy for training and prediction, and outputting the prediction results. This invention utilizes the DWT / IWT mechanism to significantly improve the accuracy of capturing small pontine infarct lesions; it achieves explicit interaction between segmentation evidence and prediction signals, significantly improving the segmentation accuracy of small lesions and the reliability of stroke risk assessment.

The present application relates to the technical field of image processing, and particularly relates to a clinical lesion auxiliary segmentation system based on nuclear magnetic resonance images. The system comprises an MRI image processing module, a lesion auxiliary segmentation module, a probability segmentation correction module and a lesion boundary smoothing module, can obtain corresponding patient clinical nuclear magnetic resonance image sets and perform image position alignment and gray scale adjustment processing, simultaneously construct corresponding clinical image lesion segmentation models for multi-scale fusion auxiliary segmentation, and generate a clinical lesion region segmentation fusion image; through the clinical lesion region segmentation fusion image, obtain the lesion confidence probability corresponding to each pixel point in the segmentation image, and correct the probability segmentation boundary of the clinical lesion region segmentation fusion image, to obtain a clinical lesion region segmentation correction result image; perform lesion edge shape smoothing processing on the clinical lesion region segmentation correction result image, to generate a clinical lesion edge shape segmentation optimization result. The present application can realize high-precision segmentation of lesions in MRI images.

The application belongs to the technical field of image processing, and discloses an image enhancement processing method for a visual bronchoscope. The method is applied to a visual bronchoscope with a distal eccentric integrated imaging module and a non-coaxial sputum suction channel inside the integrated imaging module. First, current frame original image data collected by the integrated imaging module and real-time posture data of the visual bronchoscope are acquired, and global spatial parameters are calculated in combination with pre-calibration parameters. Then, image correction is completed according to the global spatial parameters to obtain a standardized corrected image. Corresponding enhancement processing is performed on the corrected image in a regional manner, and an operation view image is obtained after feature alignment fusion and coordinate conversion, and is output in real time after optimization. The method combined with the eccentric bronchoscope can improve the definition and uniformity of the bronchoscope image, ensure the matching of the view and the operation path, and meet the clinical imaging requirements.

The invention provides a bone health assessment and fracture risk prediction system based on a cross-scale mechanical model, and the system comprises a data obtaining unit which is used for obtaining clinical image data of a user and user information data; the data processing unit is used for performing multi-scale feature extraction on the clinical image data to obtain macroscopic geometric morphology features, density distribution features, regional scale features and texture scale features of the skeleton; the mechanical response parameter calculation unit is used for calculating mechanical response parameters of the skeleton under various load working conditions on the basis of the user information data, a preset skeleton template library and macroscopic geometrical morphology characteristics and density distribution characteristics of the skeleton; and the integrated risk assessment unit is used for determining the fracture risk probability based on the regional scale features, the texture scale features and the mechanical response parameters. According to the invention, the accuracy and comprehensiveness of fracture risk assessment can be improved.

The application discloses a pneumoconiosis staging recognition method and system based on multi-modal artificial imaging feature fusion, and relates to the technical field of medical image recognition. The method comprises the following steps: standardizing and pre-processing an input chest X-ray image and extracting a lung field region; in the lung field region, detecting and counting micro nodules based on clinical imaging prior knowledge, and extracting micro nodule quantity features reflecting the quantity and spatial distribution characteristics of nodular lesions; calculating the first-order entropy features of the lung field region image to quantify the complexity of lung parenchyma texture, and extracting high-dimensional features representing gray heterogeneity and structure statistical characteristics by using an imaging feature analysis method. The multi-class artificial imaging features are standardized and fused in a unified feature space, and the fused features are input into a learning classification model for pneumoconiosis period recognition. The application improves the stability and interpretability of pneumoconiosis staging recognition through multi-modal artificial imaging feature fusion modeling.

The embodiment of the application discloses a kind of vitiligo auxiliary diagnosis methods, devices and storage medium of multi-modal medical image collaborative segmentation and classification, wherein the method comprises: obtaining the multi-modal image pair of the clinical image and Wood lamp image of the same examinee;According to the imaging characteristics of two kinds of modalities, the image pair is preprocessed and modality-specific data enhancement;The multi-modal image pair after processing is input into the feature extraction network to obtain each modality feature, and spatial guidance information for subsequent segmentation is generated;Each modality feature is input into vector quantization fusion module for cross-modal feature fusion to obtain semantic consistent fusion feature;Based on the fusion feature, a segmentation branch and a classification branch are constructed to realize the joint output of lesion region segmentation and disease activity classification, and the collaborative effect of the two tasks is improved through inter-task interaction. By using the present application, end-to-end joint optimization of vitiligo lesion segmentation and disease activity classification can be realized, and the diagnostic performance is improved.

The invention belongs to the technical field of disease screening, and particularly relates to an ear-nose-throat disease intelligent screening system and method based on multi-modal data, and the method comprises the steps: constructing an ear-nose-throat anatomical parameterized model according to a clinical image, and generating a virtual image; extracting interpretable anatomical features according to the mixed image data set, mapping the interpretable anatomical features to an ear-nose-throat disease knowledge graph, and establishing a diagnosis association rule through predicate logical reasoning; the method comprises the following steps: reading pixel features of a clinical image, matching a map rule, iterating a closed loop through a manual correction rule, and converting diagnosis adjustment into map rule supplementation; constructing a federal rule sharing framework, uploading rule correction parameters, and aggregating results through a federal average algorithm; and designing a hierarchical screening process according to the diagnosis rule model, and pushing high-confidence positive cases to superior hospitals to form a screening system. According to the invention, on the basis of virtual-real fusion data construction, a highly-adaptive and universal ear-nose-throat disease intelligent screening system is constructed through symbolized logical reasoning and federal rule sharing.

The invention discloses a non-enhanced CT kidney stone automatic detection method based on multi-scale feature fusion, and relates to the technical field of clinical image diagnos.The method includes the steps that non-enhanced CT coronal position kidney image data are collected, de-identification processing is conducted on the collected non-enhanced CT coronal position kidney image data, and the non-enhanced CT coronal position kidney image data are obtained; processing the de-identified non-enhanced CT coronary position kidney image data through a data standardization and image enhancement method, extracting key image features by adopting a convolutional neural network, and establishing a kidney stone automatic detection model based on the extracted key image features; and finally, the final automatic kidney stone detection model is deployed to a high-performance workstation with GPU (Graphic Processing Unit) acceleration capability or is integrated with the existing PACS (Picture Archiving and Communication System) of a medical institution, so that a clinical automatic detection function is realized, automatic and efficient detection of kidney stones is realized, and the detection efficiency is improved. And powerful technical support is provided for clinical diagnosis.

The invention discloses an intelligent coordination method and system for a single-model CT image reconstruction style. A training stage of the intelligent coordination method for the single-model CT image reconstruction style comprises two steps. And the inference stage is to input the to-be-coordinated source CT image, the to-be-coordinated source reconstruction style identifier corresponding to the to-be-coordinated source CT image and the target reconstruction style identifier into the trained image style coordination model to obtain a target coordination CT image. The method supports flexible conversion of multiple reconstruction styles, can adapt to a complex clinical imaging protocol, and breaks through the limitation that a traditional method is only limited to specific reconstruction styles. In addition, high-quality chain type continuous style conversion is achieved for the first time, the integrity of the anatomical structure can be effectively kept, and noise accumulation and artifact introduction are avoided. The method is specially designed for a single-type CT system, does not need to depend on multi-device and multi-center pairing data for training, and greatly reduces the threshold and cost of clinical deployment.

The invention belongs to the field of artificial intelligence, and provides a skeleton microstructure change real-time tracking decision management system, which comprises an image data access module, a dynamic structure analysis unit and an interactive decision output module, the image data access module is used for receiving skeleton three-dimensional time sequence data streams from the high-resolution microscopic imaging equipment and the clinical imaging equipment in real time; the dynamic structure analysis unit comprises a bone microstructure evolution identification module, a mechanical property prediction module and an intervention strategy generation module. Through cooperative work of the image data access module, the dynamic structure analysis unit and the interactive decision output module, full-chain closed-loop management from microstructure perception and mechanical property deduction to intelligent intervention decision is realized, and the ability of early warning and accurate intervention of skeleton diseases is significantly improved.

The invention belongs to the technical field of clinical image processing, and particularly relates to an intracranial aneurysm multi-center clinical image data standardized labeling and sharing platform which comprises a multi-center operation end and a sharing platform management end, the multi-center operation end receives and preprocesses data, labeling is carried out in an artificial dominant and AI auxiliary mode, and the sharing platform management end is connected with the multi-center operation end. The model is optimized based on local manual correction data increment; and the sharing platform management end synchronously and uniformly marks specifications, generates a standardized data set through automatic checking and expert rechecking, and performs grading sharing according to roles. According to the method, data cross-center unified multiplexing is realized, the labeling efficiency is improved, sharing and privacy security are balanced, and multi-center research and clinical diagnosis and treatment are assisted.

The invention relates to the technical field of biomedical engineering, and particularly discloses a floating catheter in-vitro simulation device based on a 3D printing model, and the device comprises a floating catheter body which is a main instrument for floating catheter examination and can be externally connected with hemodynamics monitoring equipment; the floating catheter body enters the heart and pulmonary artery individualized 3D printing model through the internal jugular vein simulation channel, and the internal jugular vein simulation channel, the heart and pulmonary artery printing model and the inferior vena cava inlet are all integrated in the neck and chest simulation part. According to the floating catheter in-vitro simulation device based on the 3D printing model, the diseased heart structure and the pulmonary artery of an individualized 3D printing patient serve as main carriers, the model can be established in an individualized mode according to clinical imaging data of different patients, and in cooperation with a liquid peristaltic pump and internal environment simulation liquid, the model can be simulated in an individualized mode; the method is more similar to individualized simulation of the anatomical structure and hemodynamic basis of a patient, and in-vitro simulation operation of floating catheter examination can be carried out.

This invention belongs to the field of image processing technology and discloses an image enhancement method for visual bronchoscopes. This method is applied to a visual bronchoscope with an eccentrically positioned integrated imaging module at the distal end and an internal suction channel that is non-coaxial with the integrated imaging module. First, the method acquires the current frame's raw image data from the integrated imaging module and the real-time attitude data of the visual bronchoscope, and calculates global spatial parameters using pre-calibrated parameters. Then, image correction is performed based on the global spatial parameters to obtain a standardized corrected image. Corresponding enhancement processing is performed on the corrected image by region, and after feature alignment and fusion, coordinate transformation, an operational view image is obtained, which is then optimized and output in real time. This method, combined with an eccentric bronchoscope, can improve the clarity and uniformity of bronchoscope images, ensure the match between the view and the operational path, and meet clinical imaging needs.

The invention relates to the crossing field of medical health and digital technology, in particular to an esophageal cancer patient nutrition status evaluation system based on digital human technology, which comprises a multi-modal data acquisition module, a digital human modeling module, a nutrition evaluation engine, a dynamic updating module and a visual interaction module, and the modules are in two-way communication through HL7 FHIR, DICOM and LIS standardized interfaces. The multi-mode module is used for collecting clinical, image, wearable, self-evaluation and intestinal flora data; the digital human model maps pathology, physiology and flora states; the nutrition engine is combined with an esophageal cancer specific model and flora characteristics to output a risk level, attribution analysis and a taboo adaptation scheme; the dynamic module triggers updating and carries out real-time early warning based on time / events; and the visualization module realizes model interaction, scheme simulation and AI recommendation. The evaluation accuracy and patient compliance are improved, postoperative complications and flora-related diarrhea are reduced, and the clinical application value is remarkable.

This invention discloses a generative AI diagnostic and treatment platform that integrates gene data and clinical images, belonging to the field of medical technology. It includes modules for data acquisition, desensitization and protection, data processing, feature extraction, gene analysis, fusion analysis, predictive analysis, treatment plan generation, result display, model management, self-iteration, and user management. This invention enables non-invasive and accurate diagnosis, reducing potential harm to newborns, minimizing unnecessary repeat examinations, lowering overall diagnostic costs, and improving the stability and accuracy of disease prediction results. Simultaneously, this invention enhances the foresight and scientific rigor of treatment plan development, providing strong support for early intervention and precision medicine, improving diagnostic efficiency and risk assessment accuracy, and saving hospitals and patients significant time and effort.

The invention discloses a multi-modal medical image-oriented cross-domain anomaly detection method. The method comprises the steps of obtaining a multi-modal medical image data set; performing medical modal cross-domain migration on the pre-trained encoder model based on a comparative learning method; extracting multi-scale features of the normal image sample by using a migration encoder; performing compression fusion on different scale features, and inputting the fused features into a decoder model for reconstruction; a training process loss function and a test process anomaly score are calculated based on the feature reconstruction error. According to the invention, auxiliary detection of lesions in various modes such as CT, MRI, X-ray and the like in clinical imaging diagnosis can be realized.

The invention discloses a myasthenia gravis drug treatment effect prediction method and device, equipment and a storage medium, and relates to the technical field of computer-aided medical diagnosis, and the method comprises the steps: obtaining clinical, image, gene and drug multi-modal data, and converting the clinical, image, gene and drug multi-modal data into a tokens sequence through preprocessing; calculating the contribution degree of each mode, and screening out an effective mode set; performing dynamic routing selection, bidirectional attention fusion and three-directional attention integration through a hierarchical multi-modal interactive attention mechanism to generate initial fusion features; performing global average pooling and vector splicing on the features to obtain target fusion features; and inputting the target fusion features and the longitudinal follow-up visit data into a time sequence causal model to obtain a treatment effect prediction result and causal chain interpretation information. According to the method, the defects of an existing method in the aspects of time sequence modeling and black box decision making are effectively overcome, longitudinal dynamic prediction of the myasthenia gravis drug treatment effect can be achieved, and prediction interpretability is improved.

The present application belongs to the technical field of medical image intelligent analysis and auxiliary diagnosis, and specifically discloses a thyroid nodule auxiliary diagnosis method and system based on multi-modal fusion, which is used for intelligent classification and auxiliary diagnosis of thyroid nodules. The method comprises the following steps: collecting and labeling medical image data and clinical structured data; standardizing and normalizing the data for pretreatment; constructing an image feature extraction branch and a clinical feature extraction branch to extract the feature representation of each modality; jointly modeling the image features and the clinical features through a strip perception module (SAC) to form a fused clinical-image context representation; generating channel-level adaptive parameters through a feature-level linear modulation method to dynamically adjust the image features individually; and finally completing the classification of thyroid nodules based on the fused multi-modal features. The present application realizes an image feature modeling method involving clinical structured data, which enables dynamic fusion of patient individualized clinical background for image interpretation, and provides more comprehensive, stable and reliable technical support for computer-aided diagnosis of thyroid diseases.

This utility model discloses a desktop X-ray machine, specifically relating to the field of X-ray machine technology. It includes a base with a mounting groove at its top. A flat panel detector assembly and a stage are housed within the mounting groove, with the stage positioned above the flat panel detector assembly. The flat panel detector assembly includes a flat panel detector, which is fixed within the mounting groove via a linear motor slide rail. An X-ray assembly, including an X-ray source, is fixedly mounted on the rear side of the top of the base. The rear end of the X-ray source is slidably connected to an arc-shaped track. This utility model synthesizes a three-dimensional image through the coordinated operation of the X-ray source and the flat panel detector, facilitating the viewing of lesion details. Its desktop, compact design reduces product size and meets the needs of two-dimensional and three-dimensional clinical imaging. The arc-shaped track and linear motor slide rail allow for independent adjustment of the X-ray source and the flat panel detector's positions, thereby improving the product's flexibility.

The individualized virtual laparoscopic surgery robot platform and device belong to the technical field of medical robots and are constructed based on clinical specific patient disease characteristics and individualized anatomical characteristics.The platform comprises an individualized three-dimensional bionic model reconstruction module, an array module of mechanical arms for simulating the operation of laparoscopic surgery, a virtual surgery feedback module for providing real-time force feedback and visual feedback, and an interactive updating and storage export module for switching the visual angle of the virtual bionic organ in real time through a high-resolution, adjustable focal length virtual laparoscope lens and interacting with the aforementioned components to operate the array module of mechanical arms and control the updating of the individualized three-dimensional bionic model.The virtual laparoscopic surgery robot platform for individualized cases has a unique multi-module interaction and system integration strategy and realizes highly realistic and individualized surgery simulation.

This application discloses a method for predicting patellofemoral arthritis based on lateral X-ray radiomics of the knee joint. First, lateral X-ray images of the knee joint and clinical information of the patient to be tested are acquired. Then, the X-ray images and clinical information are input into a clinical radiomics prediction model. The model identifies regions of interest (ROIs) in the X-ray images and extracts features from these ROIs to obtain candidate radiomics features. These candidate features are then filtered according to dimensionality reduction rules to obtain target radiomics features. The types of radiomics features include at least grayscale features, shape features, texture features, and wavelet transform features. This application, by using a prediction model to extract and filter features from ROIs in X-ray images, accurately aligns with the diagnostic criteria for patellofemoral arthritis defined by MRI, improving the reliability of the prediction. Simultaneously, by using low-cost X-ray examinations to replace high-cost MRI, patients can detect the condition earlier without compromising diagnostic effectiveness, thus reducing the burden of examinations.

The invention discloses a fully-supervised and semi-supervised coronary artery segmentation method based on a direction perception dual-tree complex wavelet, and the method comprises the steps: firstly obtaining a coronary artery computed tomography angiography image, converting the format of the image into an NIFTI format, and completing the preprocessing; then manually labeling the NIFTI format data through an interactive medical image processing system to obtain an accurate structure label of a coronary artery, constructing a coronary artery data set containing labeled data and unlabeled data, and optimizing model parameters through a composite loss function to obtain a high-performance trained XNet-DT segmentation model; and predicting the new CCTA image data by using the trained model, and outputting a coronary artery segmentation mask. According to the method, multi-direction edge perception of the blood vessel is enhanced through DT-CWT, high-precision segmentation under a small amount of annotated data is realized in combination with a semi-supervised learning mode, the problem of difficult segmentation caused by thin coronary artery branches, complex direction and fuzzy boundary is solved, a reliable coronary artery segmentation result can be directly output, and the method is suitable for large-scale popularization and application. Precise technical support is provided for clinical stenosis quantification and operation plan making, and clinical image diagnosis efficiency improvement is assisted.