236 results about "Vessel segmentation" patented technology

The invention provides a vascular calcification analysis method and device based on a non-enhanced CT image, and relates to the technical field of artificial intelligence. The method comprises the following steps: analyzing scanning parameter information of a non-contrast enhanced scanning image set; standardizing the non-contrast enhanced scanning image set based on the scanning parameter information to obtain a preprocessed image sequence; inputting the preprocessed image sequence into a multi-modal registration network, and generating a fused image based on the obtained key anatomical mark points; inputting the fused image into an attention-enhanced segmentation network, and determining blood vessel segmentation masks of the thoracic aorta, the common carotid artery and the intracranial artery; a candidate voxel set is extracted from the fused image based on the blood vessel segmentation mask, three-dimensional connected domain analysis is carried out on the candidate voxel set, and a blood vessel calcification three-dimensional area is determined; and inputting the vascular calcification three-dimensional region into the multi-task prediction model, determining a quantitative score of the vascular calcification degree, and improving the accuracy and reliability of a vascular calcification evaluation result.

The invention relates to the technical field of ophthalmologic image processing, and discloses a fundus blood vessel segmentation method based on multi-modal data. The method comprises the following steps: firstly, synchronously acquiring an optical coherence tomography image, a color fundus photographic image and a fluorescent angiography image to form multi-modal fundus data; preprocessing the eye fundus image data set to generate a standardized multi-mode eye fundus image data set; fusing heterogeneous features based on the data set, and constructing a multi-dimensional fundus feature space; using the space to train a deep learning segmentation network model, and generating an initial fundus blood vessel segmentation mask; carrying out confidence evaluation analysis on the initial mask to obtain a blood vessel segmentation result confidence distribution map; dividing blood vessel segmentation quality grades according to the distribution diagram, and defining a judgment rule; and performing interactive correction on the low-confidence region based on a rule to generate a final optimized fundus blood vessel segmentation result. And the final result is transmitted to a visual terminal for three-dimensional topology reconstruction. According to the method, the advantages of multi-modal data are integrated, and a more comprehensive fundus blood vessel segmentation result is formed.

According to various examples of the present disclosure, there is provided a machine learning image segmentation model for automatically identifying and segmenting structural features of a vessel tree from image frames. The model comprises a 3D encoder and 2D decoder, the 3D encoder and 2D decoder connected by at least one interlinked convolution node and a plurality of temporal extraction nodes therebetween. The model is configured to identify and segment structural features of a vessel tree from the plurality of image frames, by the model being configured to: receive a plurality of image frames as an input to the 3D encoder; provide an output of the 3D encoder as an input to the plurality of temporal extraction nodes to extract temporal information; generate, by the plurality of temporal extraction nodes, a 2D temporal output based on the extracted temporal information; provide the generated 2D temporal output to the at least one interlinked 2D convolution node and 2D decoder; generate, at the 2D decoder, a combined temporal output based on an output of the at least one interlinked 2D convolution node and at least one temporal extraction node, wherein the combined temporal output represents a predicted segmentation of the vessel; and generate an output representative of the segmented structural features of the vessel tree based on the predicted segmentation.

The invention discloses a three-dimensional blood vessel image segmentation method and system. Belongs to the technical field of medical image processing and particularly relates to the technical field of three-dimensional blood vessel image segmentation. The method solves the following problems existing in a blood vessel segmentation task in an existing method: small blood vessel features are difficult to accurately extract in a CTA image which is low in contrast and contains noise and artifacts; global context modeling is difficult to consider and local correlation is difficult to guarantee, so that long-distance dependent modeling is insufficient or a local structure is fractured; limited by a fixed geometrical shape of a traditional convolution kernel, the traditional convolution kernel is difficult to adapt to deformation characteristics of a complex topological structure of a blood vessel, resulting in discontinuous segmentation or fuzzy boundary of a branch region. Channel dynamic grouping and energy-driven attention generation are achieved through a grouping self-adaptive attention module, and self-adaptive modeling of a blood vessel complex branch structure and a geometrical shape is achieved through a multi-scale space structure aggregation module in combination with a strip-shaped deformable convolution and cross-scale guiding mechanism.

The invention discloses a retina image unsupervised anomaly detection method for early screening of diabetes mellitus. The method comprises the following steps: carrying out registration and multi-scale attention-guided blood vessel segmentation on a longitudinal time sequence retina image of a patient; extracting a vascular skeleton and constructing a time sequence vascular topological graph, calculating geometric morphology and hemodynamic attributes of each vascular segment, identifying vascular morphology evolution characteristics by comparing topological graphs of adjacent time points, and calculating hemodynamic characteristics such as wall shear stress through simulation; the evolution and hemodynamic characteristics are jointly input into a time sequence encoder for unsupervised learning, and an early diabetes risk score is comprehensively generated by analyzing a reconstruction error, an abnormal score based on density estimation and a time sequence trajectory deviation degree of a potential space; the scheme of the invention does not depend on lesion labels, and can sensitively detect the tiny anomalies at the early stage of pathology from multi-dimensional dynamic changes, thereby providing an objective and quantitative new way for early screening and intervention of diabetes.

Methods and systems for manually assisted definition of vascular features are described. In some embodiments, a method provides for editing of vascular paths by enabling a user to drag an erroneously segmented region of a selected vascular path into alignment with a more correctly segmented position that is depicted as a blood vessel in a vascular image. The method may use an energy function, defined as a function of position along the segmentation of the selected blood vessel, to determine how a vascular path is to be moved based on dragging motions provided by the user. In some instances, non-zero regions of the energy function are set based on the position of the selected region.

A method and system for processing an intracranial large vessel image, an electronic device, and a medium are provided. The method includes: acquiring original intracranial large vessel images of a target to be identified and a sample subject; applying a cerebrovascular segmentation model to obtain cerebrovascular mask images; computing regions of interest of the cerebrovascular mask images and bounding boxes; selecting corresponding regions of interest from the original intracranial large vessel images; separately preprocessing the mask image regions of interest and the original image regions of interest to obtain images to be processed; annotating a target region in the image to be processed of the sample subject; training a convolutional neural network model with a training set to obtain a cerebrovascular occlusion classification model; and inputting the image to be processed of the target to be identified to the cerebrovascular occlusion classification model to obtain a target region identification result.

The invention discloses a time-of-flight magnetic resonance blood vessel image cerebral vessel segmentation method, system and device and a medium, and relates to the field of medical image processing, and the method comprises the steps: obtaining a to-be-processed time-of-flight magnetic resonance blood vessel image; reinforcing blood vessel features in the to-be-processed time-of-flight magnetic resonance blood vessel image to obtain a preprocessed image; according to the preprocessed image, performing cerebrovascular segmentation by adopting a few-sample segmentation model to obtain a blood vessel probability graph; the few-sample segmentation model is obtained by migrating knowledge of a pre-training video word segmentation device to train 3D U-Net; and performing post-processing on the blood vessel probability graph and the to-be-processed flight time magnetic resonance blood vessel image based on a human-computer interaction interface and a conditional random field to obtain a final segmented image. According to the method, a high-precision and high-robustness segmentation effect can be realized only by a small number of samples, and result optimization can be carried out through an efficient man-machine interaction mode.

A system for hypertensive retinopathy (HTNR) detection includes a processor and a memory, including instructions stored thereon, which when executed by the processor, cause the system to: preprocess a retinal image using contrast enhancement, noise reduction and / or resolution normalization; segment a plurality of vessels from the preprocessed retinal image to generate a vessel segmentation map; detect a retinal marker, a vascular marker and / or an optic disc marker in the preprocessed retinal image using a first machine learning model; generate a severity score based on the detections; determine that the severity score exceeds a predefined threshold; and generate an output indicating a presence of HTNR based on the vessel segmentation map and the severity score using a second machine learning model.

The invention discloses a fundus blood vessel segmentation method based on deformable polar coordinate convolution and multi-scale gating. The fundus blood vessel segmentation method comprises the following steps: step 1, extracting multi-level features from an input image by using an encoder based on the deformable polar coordinate convolution; 2, enhancing the characteristics of the encoder by adopting a multi-scale gating attention module; and step 3, generating a blood vessel segmentation map by using the feature pyramid module based on the deformable polar coordinate convolution. According to the method, a deformable polar coordinate convolution is provided, and the offset of the deformable convolution is learned in a polar coordinate system, so that the deformable convolution can better adapt to the complex form of the blood vessel. Besides, in order to cope with the adjustment of large blood vessel scale difference and high similarity between blood vessels and background noise, the invention provides a multi-scale gating attention module, through a multi-scale strategy and a gating attention mechanism, the characterization capability of the model to multi-scale information and the anti-interference capability of the model to background noise are effectively enhanced, and the accuracy of the model is improved. Therefore, high-precision fundus blood vessel segmentation is realized.

The invention discloses a blood vessel OCT image training method, a blood vessel OCT image generating method and a blood vessel OCT image training device based on a DCN cross-modal fusion network and structure-blood flow coupling modeling. The method comprises the following steps: acquiring image data marked with a blood vessel segmentation result and a plaque position; acquiring the preprocessed OCT image data; registering the image data marked with the blood vessel segmentation result and the plaque position and the OCT image data so as to obtain registered image data; acquiring hemodynamic parameters; inputting the coronary artery CTA image, the image marked with the blood vessel segmentation result and the plaque position and hemodynamic parameters into a cross-modal fusion network based on deformable convolution so as to obtain CTA features of the cross-modal fusion network; and inputting the CTA features of the cross-modal fusion network into a trained OCT generation network so as to obtain a cross-section OCT image containing blood vessel wall and plaque information. The method can replace part of invasive OCT examination, assist doctors in assessing plaque vulnerability and guide interventional therapy.

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 a medical image optimization method and system based on vascular branch selective blurring. The method comprises the following steps: carrying out preprocessing and blood vessel enhancement on a three-dimensional angiography DICOM image; performing precise blood vessel segmentation by using an encoder-decoder network comprising a wide activation and residual cavity space pyramid module; identifying an interference branch which shields the target blood vessel structure at a specific working angle through blood vessel topology analysis; selective blurring repair is performed on the interference branches based on a Poisson equation and a bidirectional convolution LSTM to generate an optimized image which is visually unobstructed and keeps topological continuity. And a safety mechanism of virtual-real combined display and multi-angle plan planning is introduced, so that the reliability of surgical navigation is ensured. And finally, integrating the optimized 3D blood vessel model to a radiography system supporting real-time synchronization, and using the 3D blood vessel model as a road map. The clinical problem that the optimal working angle cannot be used due to blood vessel shielding can be effectively solved, and the precision and safety of an endovascular interventional operation are remarkably improved.

The embodiment of the invention provides a cross-domain generalization model training method, a blood vessel segmentation method, computing equipment, a computer readable storage medium and a computer program product. The cross-domain generalization model training method comprises the steps that source domain image data, label data corresponding to the source domain image data and target domain image data are acquired; respectively inputting the source domain image data and the target domain image data into a blood vessel segmentation model to obtain a source domain segmentation result and a target domain segmentation result; inputting the source domain segmentation result and the target domain segmentation result into a discriminator to generate a discrimination result; constructing a first loss function and a second loss function; and by taking minimization of the first loss function and simultaneous maximization and minimization of the second loss function as optimization objectives, iteratively adjusting network parameters of the blood vessel segmentation model and network parameters of the discriminator to obtain a trained generalized blood vessel segmentation model. According to the technical scheme provided by the embodiment of the invention, the blood vessel automatic segmentation precision and the cross-domain adaptive capacity are improved.

The invention relates to an automatic blood vessel segmentation method and system for a CT image. The method comprises the following steps: acquiring an enhanced CT image and a plain-scan CT image at the same position; inputting the enhanced CT image to a pre-trained first blood vessel automatic segmentation model to obtain a first blood vessel segmentation result; and registering the enhanced CT image and the plain-scan CT image to mark and map the first blood vessel segmentation result to the plain-scan CT image so as to obtain a second blood vessel segmentation result. The method has the advantages that a deep learning model (such as a registration network based on U-Net or Transform) is utilized to calculate a deformation field, and in combination with multi-scale feature enhancement and regularization strategies, the precision and stability of enhanced CT and plain-scan CT image registration are improved, and artifacts and local distortion in the deformation field are reduced; in order to solve the problem that the contrast difference of a plain scanning CT image and an enhanced CT image is significant, a compensation strategy of multi-modal texture and intensity distribution is introduced, such as adversarial loss and structural similarity index (SSIM) optimization, so that registration is more robust among different modals.

The invention provides a DSA-oriented X-ray coronary angiography image blood vessel segmentation method and system, and relates to the technical field of image processing and deep learning. The method comprises the following steps: firstly, acquiring a target segmentation image and a reference image based on X-ray coronary artery angiography video data, and forming a training sample by the reference image and the target segmentation image to construct a training data set; then constructing a segmentation network model; the segmentation network model comprises a main encoder, an auxiliary encoder, a plurality of up-sampling attention modules, a similarity attention module and a decoder; calculating a total loss function by using the segmentation mask of the target segmentation image, and optimizing the segmentation network model; and finally, inputting an X-ray coronary artery angiography image data set needing to be segmented into the trained segmentation network model to obtain a segmentation result. According to the method, an X-ray perspective image before radiography is compared with a target segmentation image, so that a neural network is helped to distinguish a foreground from a background, and blood vessels are accurately recognized.

The invention discloses a method, a device and equipment for automatically identifying a thrombus proportion. The method comprises the following steps: performing blood vessel segmentation and thrombus segmentation on a medical image to obtain a blood vessel segmentation image and a thrombus segmentation image; extracting a blood vessel center line based on the blood vessel segmentation image, and obtaining spatial position information of a point set on the blood vessel center line; clustering based on the spatial position information of each thrombus voxel in the thrombus segmentation image to form a thrombus clustering center set; and respectively determining a to-be-analyzed point set on a corresponding blood vessel center line for the thrombus clustering center. And calculating the thrombus proportion on the blood vessel section where each point included in each to-be-analyzed point set is located. Automatic segmentation of the blood vessel and the thrombus in the medical image is achieved, the core position of the thrombus is aimed at, a plurality of center points related to the position are determined from the center line of the blood vessel according to the core position, the blood vessel section is conveniently determined, and then the thrombus proportion is obtained. A large amount of time and manpower are not needed to identify, judge and mark the thrombus, and the difficulty of thrombus severity typing is effectively reduced.

The invention discloses a vein blood vessel image processing method and device, equipment and a storage medium. The vein blood vessel image processing method comprises the steps of collecting a vein blood vessel image; the collected vein blood vessel image is preprocessed; an improved U-Net segmentation network and an improved Zhang-Suen skeletonization algorithm are adopted to process the preprocessed image, and multi-modal results are fused to obtain a blood vessel segmentation result; and based on a blood vessel segmentation result, performing blood vessel three-dimensional modeling by adopting a multi-stage B-spline mapping method, and quantitatively analyzing blood vessel spatial geometric features and hemodynamic parameters to obtain a blood vessel form-function joint evaluation index. The vein blood vessel image is processed and analyzed, three-dimensional modeling is carried out on the blood vessel, the radiation path of radiotherapy is planned, and a foundation is laid for follow-up treatment of varicosity through in-vitro radiation.

The invention relates to a tumor and blood vessel three-dimensional space relation quantitative analysis method based on an enhanced CT image, and the method comprises the following steps: obtaining a thin-layer enhanced CT image, and carrying out the segmentation processing of the thin-layer enhanced CT image, and obtaining a tumor and blood vessel segmentation result; extracting a blood vessel center line from the segmentation result to generate a local plane, and reconstructing to obtain two-dimensional slices corresponding to each point on the blood vessel center line, including a blood vessel slice and a tumor slice; and based on the blood vessel section and the tumor section, determining an interaction area and calculating a wrapping angle. Compared with the prior art, the method has the advantages that the three-dimensional space relation between the tumor and the main blood vessel can be efficiently and accurately quantified, especially the wrapping angle can be calculated, and therefore more accurate and quantitative data can be provided for follow-up operation resection evaluation.

The invention relates to the technical field of blood vessel image segmentation, and provides a fundus blood vessel image accurate segmentation method based on a U-Net model, aiming at solving the problem that the existing blood vessel segmentation method based on deep learning is difficult to effectively balance global context and local details. In order to solve the problem of inaccurate blood vessel structure segmentation caused by factors such as poor image quality and low contrast ratio, a method of combining a wavelet and a visual state space model and an adaptive dynamic fusion module is provided to improve the integrity of a slender blood vessel and the structural segmentation accuracy of a complex blood vessel network in a fundus blood vessel image. By integrating multiple efficient feature extraction mechanisms, accurate recognition of small blood vessels and the whole blood vessel structure is achieved, the accuracy and robustness of blood vessel segmentation in complex medical images are remarkably improved, and the method can be widely applied to analysis of eye blood vessels such as retinopathy and hypertensive fundus lesion and research of biomarkers related to the blood vessels.

The invention provides an identification method for coronary artery vulnerable plaques, a storage medium and electronic equipment, and relates to the technical field of biomedical engineering.The identification method comprises the steps that blood vessel segmentation is conducted on a CCTA image; for each blood vessel segment obtained by segmentation, the following processing steps are performed: straightening curved surface reconstruction is performed to generate an SCPR image, and plaque detection is performed based on the SCPR image to position a first plaque area; analyzing the PCAT region corresponding to the blood vessel segment, and determining a CT value distribution diagram in spatial registration with the first plaque region; performing dynamic re-segmentation processing on the first plaque region according to the CT value distribution diagram to determine a second plaque region; and converting an image corresponding to the second plaque region, and inputting the converted image into the trained model to obtain an identification result. In the application, on the basis of the existing form-based vulnerable plaque identification, PCAT analysis is introduced to carry out joint identification, so that improvement of the overall identification performance is facilitated.

The invention provides a coronary angiography tree structure, a central line path extraction method, a central line path extraction device, equipment and a medium. The method comprises the following steps: acquiring a coronary angiography image; the coronary angiography image is input to a feature extraction module based on a Vision Transform architecture, and the feature extraction module is used for extracting the coronary angiography image; a feature extraction module based on a Vision Transform architecture is used for extracting multi-scale features; inputting the multi-scale features into an image segmentation decoder and a key point extraction decoder to generate a blood vessel segmentation result and key point coordinates; wherein the key point coordinates comprise a starting point coordinate and an end point coordinate on the path; fusing the key point coordinates, the blood vessel segmentation result and the coronary angiography image to serve as target input information; a center line path is extracted according to the target input information on the basis of a path generation algorithm of Vision Transform; and fusing the center line paths to form a coronary artery tree structure and a path map thereof.

The invention discloses a dynamic quantization recognition method for coronary stenosis based on a diffusion model and YOLO hypergraph optimization, and provides a self-supervised blood vessel segmentation method for solving the problems that manual annotation of a blood vessel mask is challenging and needs to consume a large number of resources due to the fact that blood vessel branches are fine and the structure is complex. The method comprises the following steps of: performing blood vessel segmentation by adopting a diffusion adversarial representation learning (DARL) model based on Transform; and aiming at the limitation of a traditional YOLO model in the aspect of feature fusion, difficulty in fully capturing a cross-level and cross-position complex feature relationship and incapability of providing accurate information of a narrow position, a Hyper-YOLO algorithm for locally enhancing attention is provided, so that blood vessel stenosis lesion identification can be effectively realized, and a good identification effect is achieved.

The application provides a blood vessel classification method and device, electronic equipment, medium and computer program product. The method comprises the following steps: obtaining a blood vessel segmentation result image to be processed, and the blood vessels in the blood vessel segmentation result image correspond to predicted blood vessel categories; center line extraction is performed on the blood vessels in the blood vessel segmentation result image to obtain blood vessel center lines, and an initial blood vessel tree is generated based on the blood vessel center lines; the initial blood vessel tree is converted into an undirected graph, and the undirected graph is split according to the predicted blood vessel categories to obtain a plurality of undirected subgraphs; target blood vessel trees corresponding to the undirected subgraphs are obtained based on the initial blood vessel tree, and target blood vessel categories corresponding to the blood vessels in the blood vessel segmentation result image are determined based on the target blood vessel trees. The application classifies the blood vessels in the blood vessel segmentation result image in detail with the aid of the predicted blood vessel categories, can correct the wrong classification in the original prediction, and improves the accuracy of blood vessel classification.

The invention generally provides systems and methods for performing vessel segmentation from flow data, such as but not limited to 4D flow Magnetic Resonance Imaging (MRI) data. In certain aspects, the systems and methods of the invention may involve receiving flow data representative of flow in a vessel (such as 4D MRI flow data); identifying net flow effects in the flow data (such as 4D MRI flow data) according to a standardized difference of means (SDM) velocity that involves quantifying a ratio between net flow and observed flow pulsatility in each voxel of the received flow data (such as 4D MRI flow data); and identifying voxels with higher SDM velocity values than stationary tissue voxels, thereby performing vessel segmentation from flow data (such as 4D MRI flow data).

The invention relates to the field of fundus photograph enhancement and segmentation, and provides a fundus photograph enhancement segmentation method and device based on Diffusion, and the method comprises the steps: obtaining a fundus photograph, inputting the fundus photograph into a pre-trained Diffusion-based combined image enhancement segmentation model, and obtaining a fundus photograph enhancement image and a fundus photograph retinal blood vessel segmentation image; the model comprises a Diffusion-based image enhancement branch, an image segmentation branch and a symbiotic information interaction module, and the symbiotic information interaction module is used for realizing information flow between the image enhancement branch and the image segmentation branch, so that the image enhancement branch generates a fundus color photograph enhancement image based on a fundus color photograph and information from the image segmentation branch. Meanwhile, the image segmentation branch generates a fundus color photoretinal blood vessel segmentation image based on the fundus color photograph and information from the image enhancement branch. According to the invention, the understanding and analysis of the model on the image can be improved, and accurate blood vessel segmentation is provided while the image quality is improved.

The application discloses a blood flow reserve fraction determination method, device, system and storage medium. The method comprises the following steps: acquiring an initial blood vessel image, inputting the initial blood vessel image into a pre-trained blood vessel segmentation model to obtain a first blood vessel segmentation model; determining a stenosis region of the first blood vessel segmentation model, performing a de-stenosis processing on the stenosis region of the first blood vessel segmentation model to obtain a second blood vessel segmentation model; acquiring flow data of the stenosis region of the first blood vessel segmentation model and flow data of a de-stenosis region of the second blood vessel segmentation model; and determining a blood flow reserve fraction based on the flow data of the stenosis region of the first blood vessel segmentation model and the flow data of the de-stenosis region of the second blood vessel segmentation model. The technical solution can effectively reduce the error of the determined blood flow reserve fraction by determining the blood flow reserve fraction based on the flow data of the stenosis region of the first blood vessel segmentation model and the flow data of the de-stenosis region of the second blood vessel segmentation model.

The present application belongs to the field of medical image segmentation, and particularly relates to a retinal blood vessel image segmentation method. In view of the problems of low segmentation accuracy, insufficient segmentation ability of small blood vessels at the edge of eyeball, fracture at the blood vessel branch, and excessive interference of image noise in the existing retinal blood vessel image segmentation, the method comprises the steps of retinal image preprocessing and establishment of a retinal blood vessel segmentation model, wherein the preprocessing comprises converting a color retinal image into a gray image by giving different weights to the RGB three channels of the color retinal image; using a normalized and contrast-limited adaptive histogram equalization method to improve the image; using a local adaptive gamma change algorithm to adjust the retinal image; using translation, rotation, and noise increase to expand the data set; and the model establishment comprises feature extraction, feature fusion, and retinal blood vessel image segmentation.

The embodiment of the invention provides a blood vessel segmentation method, computing equipment, a computer storage medium and a computer program product. The blood vessel segmentation method comprises the following steps: acquiring original medical image data containing blood vessels; performing feature extraction on the original medical image data to generate image feature data; based on the image feature data, modifying a pixel intensity value of a pixel in the original medical image data to obtain first medical image data; performing edge detection on the first medical image data to obtain edge information of the blood vessel, and generating second medical image data containing a blood vessel boundary mask; and based on the blood vessel boundary mask, performing blood vessel segmentation on the second medical image data to obtain a target blood vessel image. The technical scheme provided by the embodiment of the invention can improve the accuracy of blood vessel segmentation.

The invention discloses a method, device and equipment for repairing a blood vessel fracture in a CAT blood vessel segmentation image. The method comprises the following steps: acquiring the CAT blood vessel segmentation image and two connection points of a marked fractured blood vessel; performing connected domain analysis on the CAT blood vessel segmentation image, separating at least one component image, and determining a target component image where two connection points are located; extracting a skeleton of the target component image to obtain skeleton points on a blood vessel center line, and constructing a blood vessel skeleton mapping a blood vessel center line structure based on the skeleton points; determining a blood vessel fracture type according to the target component image, and searching a fractured blood vessel starting point corresponding to each connection point by adopting a corresponding starting point searching strategy; and tracking the center line of the target blood vessel based on the searched starting point of the fractured blood vessel, and imaging the fractured blood vessel based on the tracked center line of the target blood vessel. Therefore, the problems that an existing blood vessel fracture connection method is low in calculation efficiency and cannot adaptively select a proper operation method according to the specific blood vessel form are solved.