388 results about "Blood vessel structure" patented technology

A method of visualizing structural information of pulmonary vessels in medical images includes obtaining structural information of pulmonary vessels based on anatomical structure features of the pulmonary vessels from a medical image based on segmentation results of a pulmonary vascular region of the medical image; obtaining pulmonary embolism finding information in the pulmonary vessels of the medical image; visualizing the structural information of the pulmonary vessels in accordance with hierarchical information; and visualizing the pulmonary embolism finding information in association with individual sub-regions of the structural information of the pulmonary vessels visualized in accordance with the hierarchical information.

The invention relates to the technical field of orthopedic diagnosis and treatment, in particular to a monitoring method and system for dynamically monitoring the lower limb vein blood flow state and a storage medium. The method comprises the following steps: acquiring a lower limb vein ultrasound image sequence of a target patient, and establishing a blood vessel space probability distribution map based on anatomical priori knowledge; encoding the blood vessel space probability distribution map into conditional prior information, and constructing a blood vessel ontology knowledge base; constructing a multi-level diffusion probability model according to the blood vessel ontology knowledge base; and performing vascular structure reconstruction and blood flow feature extraction on the lower limb vein ultrasound image sequence by using the multi-level diffusion probability model to obtain blood vessel fluid feature data. Vascular positioning, branch recognition and morphological analysis are completed step by step from macroscopic to microcosmic through the three-level diffusion model, blood flow dynamic information is extracted in combination with time sequence features, and comprehensive representation of a vascular system is achieved.

The invention discloses a capillary image evaluation method, device and equipment and a medium, and the method comprises the steps: obtaining IQ data of a to-be-detected region, and carrying out the beam forming of the IQ data; performing clutter filtering processing on the IQ data of each continuous data frame after beam forming, and extracting a blood flow signal; removing background noise from the blood flow signal to obtain a blood vessel structure image, and segmenting the blood vessel structure image to obtain a distribution position of a blood vessel region; screening a peak region based on the distribution position of the blood vessel region, and determining a peak center position; trajectory tracking is carried out based on the peak value center position, and all trajectories are superposed and displayed to generate a capillary image; and analyzing the generated capillary image, and extracting related parameters of the capillary. Therefore, the method is not limited by image resolution, and can be applied to microvascular images with limited resolution and super-resolution microvascular images, and vascular morphology and hemodynamic parameters can be extracted from the microvascular images with limited resolution and super-resolution.

The invention discloses a heart mitral valve medical image segmentation model, a training method, a segmentation method and equipment. The segmentation model is improved on the basis of a Double U-Net architecture, and two independent U-Net networks which are connected in series respectively comprise an encoder and a decoder; an OfficientNetB7 network is used as a feature extraction encoder of the front end U-Net network; after features are extracted from the two U-Net networks, a multi-kernel residual error convolution (MKRC) module, a cavity space convolution pyramid pooling (ASPP) module and a mixed triple attention (TAG) module are sequentially introduced into the two U-Net networks; a triple attention gate (TAM) module is introduced into the input end of the decoder. Segmentation model training adopts a mixed loss function, binary cross entropy loss and Dice loss are combined, the class imbalance problem is balanced, and segmentation precision is improved. The heart and related blood vessel structures can be automatically and accurately segmented, so that the technical problem that heart mitral valve image segmentation is difficult to accurately and efficiently perform in the prior art is solved.

The invention relates to the technical field of medical image analysis, and discloses an HER2 positive breast cancer auxiliary prediction method based on multi-modal data fusion. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

The invention relates to the technical field of image analysis, in particular to a three-dimensional hepatic duct, hepatic artery, portal vein and hepatic vein image segmentation method and system. The method comprises the following steps: acquiring multi-phase medical image data of a liver region; performing region identification on the image data, and extracting hepatic duct, hepatic artery, portal vein and hepatic vein regions; executing structure tracking on the hepatic duct area to obtain a hepatic duct positioning result; carrying out improved contrast extraction on the hepatic artery region to obtain hepatic artery positioning data; performing directional segmentation analysis on the portal vein region to obtain a portal vein positioning result; performing branch angle extraction on the hepatic vein region in combination with the portal vein direction to obtain hepatic vein positioning data; taking the hepatic artery as a reference structure, performing elastic registration on each blood vessel structure, and generating registration data under a unified coordinate; and completing three-dimensional visualization based on a registration result. According to the method, multi-stage image joint segmentation and registration are realized, and the method has the technical advantage of high structural recognition integrity.

A vascular image processing method performed by a processor includes extracting, from a vascular image, a first vascular region corresponding to an entire blood vessel included in the vascular image, a second vascular region corresponding to a target vessel and one or more branch vessels connected to the target vessel, and a third vascular region corresponding to the target vessel, and predicting a vascular structure in the vascular image, based on the first vascular region, the second vascular region, and the third vascular region, which are extracted from the vascular image.

The invention provides a lung CT lesion and related factor correlation analysis method, which comprises the following steps: establishing a dynamic compensation model according to blood glucose level fluctuation data of a patient, adjusting CT scanning parameters by monitoring a blood glucose value in real time, and compensating tissue density change in a scanning process to obtain corrected original image data; performing microvascular structure enhancement on the preliminarily reconstructed CT image, extracting blood vessel features, calculating the probability that each voxel belongs to a blood vessel structure, and performing enhanced display on a microvascular network in combination with local structure tensor analysis; performing fine segmentation of the leaflet spacing and the microbronchus wall on the image after the microvessel enhancement, extracting a fine structure of the lung through multi-scale feature fusion, and performing post-processing to optimize a segmentation result; and performing anomaly detection on the whole lung area, extracting characterization characteristics of normal and abnormal tissues, and accurately positioning a suspicious focus area in combination with a multi-instance learning strategy, thereby providing auxiliary diagnosis information for early lung cancer screening.

The invention relates to the field of cardiovascular medicine, and discloses a coronary artery vulnerable plaque detection and analysis system which comprises a data collection module used for collecting multi-mode imaging data of a coronary artery and identifying the blood vessel structure and the blood vessel type of the coronary artery, and a plaque area identification module used for positioning the branch opening position of the coronary artery and identifying the blood vessel structure and the blood vessel type of the coronary artery. Multi-class plaques of coronary arteries are separated out; the plaque classification detection module is used for extracting structural features of multiple categories of plaques, identifying vulnerable plaques and stable plaques in the coronary artery and setting a plaque classification detection system of the coronary artery; the vulnerable plaque evaluation module is used for constructing a coronary artery vulnerable plaque scoring mechanism according to the plaque vulnerability index, the plaque component and the radial wall strain value; and the result feedback module is used for generating a vulnerable plaque feedback result of the coronary artery in combination with the vulnerable plaque scoring mechanism and the plaque classification detection system. According to the invention, the accuracy of coronary artery vulnerable plaque detection can be improved.

The invention discloses a blood vessel continuous segmentation method based on a graph network, and relates to the technical field of blood vessel continuous segmentation, multi-scale texture features based on coronary artery influence and topological structure features of blood vessels are fused, and correlation among different features is enhanced through an attention mechanism; segmenting the fused multi-scale texture features influenced by the coronary artery and topological structure features of the blood vessel, extracting multi-scale features, and reducing the resolution; gradually recovering the resolution by using a decoder to obtain a segmentation result, and optimizing the network weight by using an error between the segmentation result and a real label; and applying the trained segmentation network to test data to obtain a three-dimensional segmentation result of the coronary artery, and evaluating the accuracy of the segmentation result by comparing the difference between a predicted value and a true value to obtain connection constraint loss. The problem that a three-dimensional blood vessel structure is difficult to extract by a general medical segmentation model is solved, so that a blood vessel segmentation result is more continuous and more accurate.

The invention relates to the technical field of lung perfusion, in particular to an AI-based lung perfusion evaluation system, which comprises an image quality screening module, a blood vessel positioning analysis module, a blood flow velocity measurement module, a blood vessel anomaly analysis module and an anomaly response module, and is characterized in that the image quality screening module monitors the pixel density of an image and the variation amplitude of color gradient. According to the method, through real-time image definition evaluation and screening, the image selection process is optimized, it is ensured that all the images used for analysis reach the high-quality standard, the gray level change and the edge contour of the blood vessel are automatically calculated, the potential lesion area is accurately positioned and marked, more detailed blood vessel structure analysis is provided, and the accuracy of blood vessel analysis is improved. In addition, the system can rapidly analyze the speed deviation of the blood vessel segment, timely identify the abnormal blood flow, effectively improve the judgment speed of diseases such as pulmonary embolism or pulmonary hypertension, enhance the efficiency of coping with emergency medical conditions through automatic abnormal detection and recording, improve the accuracy and speed of judgment, and support more effective clinical decisions.

The invention relates to the technical field of medical image processing, in particular to an automatic blood vessel extraction method and system based on a CT image, and the method comprises the steps: 1, constructing a blood vessel local coordinate system, and carrying out the double-domain separation; 2, dynamic double-domain fusion, wherein contrast self-adaptive weighted fusion is carried out on the spatial domain enhanced image and the fundamental frequency blood vessel image; 3, direction constraint morphological reconstruction: constructing a structural element with a long axis parallel to the direction of the blood vessel according to the main direction vector v of the blood vessel; sequentially executing directional opening operation and directional closing operation along the direction of the main direction vector v of the blood vessel; and 4, anatomically-driven blood vessel repair and correction: tracking contours of adjacent slices along the direction of the main direction vector v of the blood vessel and fitting a repair path on the basis of a generated calcified region mask Mcal marked region, and correcting a topological structure at a bifurcation point according to a branching angle and hemodynamic constraint. The vascular structure can be accurately and stably extracted from the CT image by comprehensively utilizing restoration technologies such as spatial domain enhancement and frequency domain separation.

The invention discloses a blood vessel segmentation method based on an improved U-shaped network, and the method comprises the steps: S1, obtaining a fundus image data set, carrying out the data enhancement of a to-be-segmented fundus image, obtaining a preprocessed fundus image, and dividing the preprocessed fundus image into a training set and a test set; s2, constructing a fundus image segmentation convolutional neural network model for accurately segmenting retinal blood vessels in the fundus image; s3, training the fundus image segmentation convolutional neural network model by using the preprocessed fundus image until the network model converges; and S4, inputting the fundus image to be segmented into the trained fundus image segmentation convolutional neural network model to obtain a fundus image retinal vessel segmentation result. According to the method, fine blood vessels and low-contrast blood vessel structures can be accurately segmented, and generalization and robustness of a network model are improved.

The invention relates to the technical field of medical image processing and tumor diagnosis, in particular to a tumor recognition method based on multi-modal information fusion. The method comprises the following steps: acquiring tumor multi-modal information of a target object, wherein the multi-modal information comprises tumor image data, tumor genomics data and individual tumor related information; the tumor-related information comprises a vascular structure, a blood flow velocity, fluid viscosity and fluid pressure of the target object; determining a tumor cell density, a diffusion coefficient and a cell proliferation rate according to the tumor image data and the tumor genomics data; predicting a diffusion path of the tumor in the local tissue according to the tumor cell density, the diffusion coefficient and the cell proliferation rate; predicting a metastasis path of tumor cells through a vascular system according to the individual tumor related information; and according to the diffusion path of the tumor in the local tissue and the metastasis path of the tumor cells through the vascular system, identifying the potential metastasis site of the tumor.

The invention provides an angiographic catheter with a hydrophilic lubricating coating and application of the angiographic catheter. The angiographic catheter with the hydrophilic lubricating coating comprises a catheter seat, a catheter body, a transition section and a head end, the main body part of the pipe body consists of three layers, namely an inner layer, a braid layer and an outer layer; wherein the outer-layer material is designed in sections, is designed according to a physiological anatomical structure of a blood vessel, and provides corresponding flexibility according to different sections of blood vessels; the composite material is mainly prepared by matching one or more of nylon 12, PEBAX and TPU polyester materials; the outer layer of the catheter is provided with the hydrophilic lubricating coating, corresponding flexibility and pushing performance are provided, the catheter can effectively adapt to the blood vessel structure, the catheter can conveniently reach the designated position, and thrombus can be inhibited to a certain degree.

The invention relates to the technical field of medical image segmentation, in particular to a medical image segmentation method combining a convolutional neural network and a graph network, and the method comprises the following steps: forming a data set CoronarySet by using coronary artery CT angiography three-dimensional image data collected by a computed tomography (CT) device, and randomly dividing the data set CoronarySet into a training set CoronarySet 1 and a test set CoronarySet 2; the method comprises the following steps of: segmenting data of a training set CoronarySet1 into a plurality of patches; inputting the patchings into a double-branch parallel encoder to obtain a multi-layer coronary artery texture feature map and a multi-layer coronary artery topological feature map; constructing a feature fusion module, and fusing the texture feature maps and the topological feature maps of different layers based on an attention mechanism module to obtain fused blood vessel features; shallow layer features of a three-dimensional convolution module and fused blood vessel features are sequentially input into each layer of a segmentation decoder, a three-dimensional blood vessel structure can be extracted, and a blood vessel segmentation result is more continuous and more accurate.

The invention discloses an automatic retinal vessel segmentation method fusing large-kernel multi-scale cavity convolution and an attention mechanism, which is suitable for accurate extraction of a fine-grained vessel structure in a fundus image. According to the method, an improved deep neural network (LKD-UNet) is constructed, a collaborative receptive field module is embedded in each layer of an encoder, and large kernel convolution and multi-scale cavity convolution are combined to enlarge the receptive field and enhance the texture analysis capability; an efficient Transform module is introduced into a bottleneck layer, a cross-regional semantic relationship is modeled through a multi-head self-attention mechanism, and the topological consistency of microvessels is improved; the decoder adopts a shallow symmetric structure and a kernel scale scheduling strategy, and realizes high-resolution reduction in combination with jump connection. In addition, a four-category pixel-level error visualization mechanism is introduced and is used for assisting segmentation error diagnosis and result interpretation. Experimental results show that the method is superior to an existing mainstream model in multiple public data sets, has remarkable advantages in the aspects of segmentation precision, structural coherence and deployment efficiency, and is suitable for remote fundus screening and blood vessel extraction tasks in embedded medical equipment.

The invention relates to the technical field of operation planning, in particular to an adaptive drainage basin segmentation and operation planning system based on deep learning, which comprises a chest imaging data modal layer used for acquiring chest imaging data modal information including lung lobes, lung segment boundaries and lung blood vessels; the end-to-end multi-task deep learning model layer is in communication connection with the chest imaging data modal layer and comprises a blood vessel segmentation module used for performing blood vessel segmentation on the chest imaging data modal; the hierarchical drainage basin segmentation module is used for carrying out hierarchical drainage basin segmentation based on the blood vessel segmentation result; the topological basin network module is used for analyzing the topological relation of the vascular structure; the drainage basin visualization module is used for generating visualization results of lung lobes and lung segments; the path planning module is used for generating an operation scheme; the evaluation and decision-making module is used for carrying out evaluation and decision-making analysis on the operation scheme; according to the invention, full-process automatic processing from medical image input to surgical scheme output is realized.

The invention relates to the technical field of medical image analysis, and discloses a cardiovascular disease detection method based on image analysis. The method comprises the steps of obtaining a medical image sequence of a target patient, and extracting a blood vessel region contour to generate an initial blood vessel topological graph; the initial topological graph is registered with a standard cardiovascular model, the curvature deviation degree and the pipe diameter variation coefficient of each blood vessel branch are calculated, and a blood vessel elastic characteristic matrix is generated in combination with abnormal displacement nodes; and performing multi-scale fusion on the curvature deviation degree, the pipe diameter variation coefficient and the elastic characteristic matrix, outputting a vascular structure anomaly index, and generating a hemodynamic parameter set. Dividing risk areas according to gradient distribution of the parameter set on the three-dimensional model, performing texture co-occurrence matrix analysis on pixel clusters in the high-risk areas, and extracting texture fingerprints of calcified plaques and lipid deposition. And determining the type and severity level of the cardiovascular disease according to the peak value distribution. According to the invention, accurate and objective detection and risk assessment of cardiovascular diseases are realized.

The invention relates to the field of medical image processing, in particular to a medical image segmentation method, system and device based on fuzzy logic modeling and a dynamic graph convolutional network, and a medium, and the method comprises the following steps: S1, constructing a BT-Net 3D U-shaped deep network integrated with a boundary integrity enhancement module and a trajectory continuity enhancement module; s2, extracting input features; s3, a boundary integrity enhancement module obtains enhanced boundary features based on uncertain estimation and a fuzzy neural network; the track continuity enhancement module obtains enhanced continuity imaging features based on the dynamic graph convolutional network; and S4, performing feature reconstruction to obtain a final segmentation result. According to the method, uncertainty is reduced based on fuzzy logic modeling, the integrity of the blood vessel boundary can be iteratively enhanced, so that the ability of the network to determine the blood vessel boundary under complex conditions is improved, the continuity of the blood vessel trajectory is iteratively enhanced based on the dynamic graph convolutional network, and the ability of the network to process cross-scale complex micro blood vessel structures is improved.

The invention discloses a vascular respiratory movement map construction method, device and equipment and a medium, and relates to the technical field of medical treatment, the method establishes a vascular digraph based on a topological connection relationship in a vascular structure, and quantifies the overall displacement of a blood vessel and the posture of each branch through the extraction of structure control points and the calculation of branch bending energy. The propagation direction of motion between branch points is defined, and consistency constraint is provided for vascular motion modeling. Vascular deformation in motion modeling is quantified by adopting adaptive deformation compensation, and motion deformation of each section of a branch is represented by using rotation measurement and stretching measurement under a spherical coordinate system of a manifold space. Meanwhile, the bending energy weight is adaptively distributed for each branch based on the vascular graph and the SRVF constraint, and the learning efficiency of the model for local attitude deformation is improved.

The invention discloses a method for removing reflection of inner conjunctiva in an eye fundus image. The method specifically comprises the following steps: collecting a sample picture for preprocessing; constructing a reflection removal model, wherein the reflection removal model comprises a reflection detection module, a blood vessel retention module and a reflection elimination module; processing the sample picture through a light reflection detection module and a blood vessel retention module, obtaining light reflection positioning features and blood vessel features, and then obtaining fusion features; generating a target picture by using a reflection elimination module; and then model training and reasoning are carried out to obtain an eye fundus image with inner conjunctival reflection removed. According to the method, under the condition that the fundus image with the reflective light is given, the reflective area can be positioned, the reflective light is removed, the vascular structure covered by the reflective light is recovered to a certain extent, the problem that the fundus image shot by a mobile fundus imager is not easy to use due to the reflective light of the inner conjunctiva is effectively solved, the automatic reflective light removal function is achieved, and the efficiency is improved. The fundus image imaging cost is effectively reduced, and the production efficiency is improved.

The invention discloses a retinal blood vessel image segmentation method based on dynamic weight distribution and index guide optimization. Comprising the steps that a to-be-segmented retinal blood vessel image is input into an LM-TransUNet model, an image segmentation visualization result is obtained, the LM-TransUNet model is obtained based on training of a training set, and the training set comprises the retinal blood vessel image and a corresponding true value; the process of training the LM-TransUNet model based on the training set comprises the following steps: predicting a retinal blood vessel image through an OfficientNet and ResNet hybrid encoder model, calculating segmentation loss and performance index loss according to a prediction result and a truth value, adaptively fusing multi-scale features by using a dynamic weight distribution mechanism, and adjusting network parameters in real time through an index guide optimization module, so as to achieve the purpose of improving the performance of the retinal blood vessel image. And a new prediction result is obtained for iteration until convergence, and a final model is obtained. According to the method, the accuracy of retinal blood vessel image segmentation can be remarkably improved, and particularly, the method is excellent in segmentation of complex blood vessel structures and small blood vessels.

The invention discloses a medical blood vessel segmentation dynamic optimization method and system based on anatomy prior perception, and belongs to the field of artificial intelligence and the technical field of medical image processing. The method comprises the following steps: acquiring medical image data and corresponding blood vessel labeling data, constructing a data set, and preprocessing and post-processing data in a training set; constructing a segmentation model, wherein the segmentation model comprises a multi-scale feature fusion module and a dynamic snakelike convolution module; constructing a loss function of the segmentation model, and training the segmentation model by using the training set and the blood vessel labeling data; and performing blood vessel segmentation on the medical image data in the test set by using the trained segmentation model. According to the invention, through the dynamically configured multi-scale feature fusion module and fusion mechanism and the dynamic optimization strategy of topology perception, the problem that it is difficult to consider fine segmentation of the blood vessel structure and overall topology integrity in the prior art is effectively solved, and the robustness and operation efficiency of the medical blood vessel segmentation model are significantly improved.

The invention discloses an adjustable thrombus breaking device, and belongs to the technical field of interventional therapy instruments, the adjustable thrombus breaking device comprises a guide wire tube and a guide wire, the tube wall of the guide wire tube is provided with a guide wire port, and the guide wire port is located at the position close to the far end of the guide wire tube; the guide wire is arranged in the guide wire tube in a penetrating mode, the far end of the guide wire is connected with the far end of the guide wire tube, the guide wire can penetrate out of the guide wire opening in a bent shape, and the part, penetrating out of the guide wire opening, of the guide wire forms a broken plug structure. The guide wire can penetrate out of the guide wire opening formed in the guide wire tube, the thrombus breaking structure is formed by the guide wire penetrating out of the guide wire opening, the outer diameter of the thrombus breaking structure can be adjusted through the guide wire, and the thrombus breaking device has the advantages of being simple in structure, high in flexibility and high in thrombus breaking efficiency and can adapt to complex blood vessel structures and suction catheters with small catheter diameters.

The invention relates to the technical field of medical image analysis, and discloses a breast cancer treatment response prediction system based on multi-time-sequence images. According to the method, preoperative MRI, clinical pathology data and RNA-seq data meeting quality control standards are obtained through dynamic case screening, traditional image features, multi-scale Hessian matrix blood vessel-structure features and topological persistent coherent curvature features are extracted through multi-plane segmentation, and key features are obtained through three-level screening. A multi-factor logistic model fusing radiomics, clinical features and immune scoring is further constructed, a pCR probability value and a feature contribution heat map are output, and MRI data quality control, real-time analysis and interactive report generation are achieved through a matched computer system. The method breaks through the limitation of single modal analysis, dynamically associates the image features with the molecular mechanism, and provides interpretable decision support for precision medical treatment.

Systems, methods, and computer program products for predicting and detecting the onset of retinal diseases are provided. A method of detecting glaucoma includes: pre-training at least one neural network model of a plurality of neural network models based on a small data classifier; training the plurality of neural network models based on a plurality of indications of glaucoma based on retinal data including at least two of a peripapillary atrophy value, a disc hemorrhage value, and a blood vessel structure analysis value; simultaneously generating a risk score associated with each of the plurality of indications based on the trained plurality of neural network models; combining the risk score associated with each of the plurality of indications based on a classification model to produce a likelihood of glaucoma; and determining whether glaucoma is present based on the likelihood of glaucoma.

An implantable system for monitoring a vascular structure can include a sensor configured to be positioned within the vascular structure and further configured to detect a property of a flow of blood in the vascular structure, a communication circuitry configured to be positioned within the vascular structure, the communication circuitry connected to the sensor and configured to transmit data related to the property of the flow of blood detected by the electronic sensor, and an antenna configured to be positioned within the vascular structure, the antenna connected to the communication circuitry and further configured to facilitate transmission of the data related to the property of the flow of blood detected by the sensor.

The invention discloses a human body peripheral blood vessel photoacoustic three-dimensional imaging device and an imaging method thereof, and belongs to the field of blood vessel imaging. Through high resolution and functional monitoring capability of photoacoustic imaging, detailed blood vessel structure and blood flow dynamic information are provided, and early recognition and treatment effect evaluation of common lesions such as diabetic foot and peripheral artery diseases are promoted; meanwhile, a mechanical system is adopted, so that the detection front end with relatively high quality can be borne, and stable operation is realized, and a relatively good photoacoustic imaging result is obtained; the system has a highly stable accurate scanning imaging mode, and is convenient for multiple imaging modes of clinical application; through the arrangement of the imaging water tank and the lifting structure of the imaging water tank, switching of imaging modes in a limited space can be ensured, so that the device can be suitable for the layout of a portal type three-dimensional moving device. The system is expected to promote the portability of equipment, reduce the research and development cost of the equipment, be widely applied to clinical practice and community medical treatment, and improve the disease management level.

The invention relates to the technical field of image modeling, in particular to a three-dimensional modeling method and system for medical image data, and the method comprises the steps: carrying out the preprocessing of the obtained two-dimensional or three-dimensional medical image data, extracting a blood vessel region based on the image texture and gray features, and generating a preliminary blood vessel structure segmentation image; in combination with a blood flow simulation or real acquisition means, obtaining dynamic feature data from the vascular structure segmentation map, and mapping the dynamic feature data to a corresponding vascular structure position to form a vascular distribution map with blood flow constraint; and inputting the blood vessel distribution diagram into a three-dimensional modeling module, expanding and complementing small blood vessel branches by utilizing blood flow constraint, dynamically correcting the form of a main blood vessel, and generating a three-dimensional blood vessel structure model with real fluid physiological characteristics. The method can be widely applied to medical research and clinical aid decision-making scenes such as preoperative simulation, individualized intervention path planning and blood flow reconstruction analysis, and has good popularization and application prospects and engineering practical value.