23 results about "Vascular segmentation" patented technology

The present invention discloses a vascular segmentation model and method. The vascular segmentation model includes an encoder and a decoder. The encoder extracts features from computed tomography images to obtain a basic neural network feature map. The decoder includes a vascular perception module and an upsampling layer. The vascular perception module fuses prior knowledge of blood vessels with the basic neural network feature map to output a vascular segmentation map. The upsampling layer restores the vascular segmentation map processed by the vascular perception module to a size corresponding to the CT image. The technical solution of this application can effectively improve vascular segmentation accuracy.

The application discloses a kind of nnUNet zebra fish juvenile whole brain vascular system segmentation methods based on autonomous data set training, it is related to high-resolution imaging technology, image processing and medical image segmentation field, the method makes full use of zebra fish live transparency and fluorescent label advantage, obtains high-resolution whole brain three-dimensional vascular image data, and constructs high-quality segmentation truth value database by semi-automatic segmentation and artificial correction, training is carried out using nnU-Net deep learning model, realize the three-dimensional automatic segmentation of zebra fish brain vascular system signal.The application method significantly improves the degree of automation and precision of image segmentation, effectively solves the problems of low efficiency, high artificial dependence and poor repeatability of traditional brain vascular segmentation.The method is suitable for large-scale high-throughput data processing, can provide efficient, standardized image processing scheme for zebra fish brain vascular development mechanism and brain vascular disease model research, and has wide application prospect.

The invention belongs to the technical field of medical image processing and neural image analysis, and particularly discloses a cerebral apoplexy prognosis prediction method which comprises the following steps: step 1, performing whole cerebral vessel segmentation and morphological feature extraction on a CTA image to obtain 40 blood vessel morphological features; 2, extracting high-throughput omics characteristics from an infarction core and a half-dark band region in the DWI image, and obtaining 1026 image omics characteristics; 3, screening high-resolution features based on minimum absolute contraction and a selection operator, establishing a support vector machine classification model, and predicting a prognosis classification result of a three-month improved Rankin scale of the stroke patient; the deep learning network is used for automatically segmenting the whole brain blood vessel, so that subjective difference of manual recognition is reduced; starting from pathophysiology of occurrence and development of cerebral apoplexy, vascular morphological characteristics and infarction region imageomics characteristics are comprehensively incorporated to carry out prognosis prediction on cerebral apoplexy.

A method of nidus segmentation includes: acquiring a medical image to be processed; performing a vessel segmentation on the medical image to be processed to obtain a first vessel segmentation result; extracting a first centerline of a first vessel according to the first vessel segmentation result; cutting the medical image to be processed for several times to obtain a plurality of first sections and further obtain a first object medical image by synthesizing the plurality of sections, and cutting the first vessel segmentation result for several times to obtain a plurality of second sections and further obtain a first object vessel segmentation result by synthesizing the plurality of second sections; and obtaining a nidus segmentation result based on the first object medical image and the first object vessel segmentation result.

The invention discloses a cerebral vascular hemodynamic measurement method and device based on transcranial color Doppler. The method comprises the following steps: training a plurality of sample images to establish a multi-target cerebrovascular segmentation model; inputting a to-be-segmented image into the multi-target cerebrovascular segmentation model to obtain a segmentation region of each cerebrovascular in the to-be-segmented image; performing central axis transformation on the segmented region of each cerebral vessel, and extracting a skeleton structure of each cerebral vessel; and based on the skeleton structure of each cerebral vessel, calculating to obtain an optimal measurement position and an optimal measurement angle for measuring cerebral vessel hemodynamic parameters. According to the method, more accurate analysis can be provided for diagnosis and evaluation of cerebral vessels under transcranial color Doppler, errors caused by manual operation are reduced, and improvement of accuracy and reliability of medical image analysis is facilitated.

The vessels of a biological object are to be segmented more reliably. To this end, a method for training a machine learning algorithm to segment such vessels is proposed. First, a 3D reconstruction (8) of the vessels is provided. A starting vessel region (7) is identified in the 3D reconstruction (8). Sub-regions representing a starting vessel segment are extracted from the starting vessel region (7). The algorithm is trained using these extracted sub-regions. Subsequently, the trained algorithm is applied to a first neighboring region (10) immediately adjacent to the starting vessel region (7). This identifies a first vessel segment in the first neighboring region (10). Finally, the algorithm is retrained using the first vessel segment identified in the first neighboring region (10).

The invention relates to the technical field of blood vessel image processing, in particular to a blood vessel processing optimization method based on StyleGAN data enhancement and a retina image. According to the method, data enhancement, blood vessel segmentation and classification training are organically combined through complete process design. StyleGAN is utilized to generate a synthetic retina image expansion data set, the problem of data imbalance is relieved, and the generalization ability of the model is improved; precise blood vessel segmentation is achieved through IterNet, classification training is completed through a FullSizeUNetBlock model, retina images are systematically processed in the whole process, a closed loop is formed from data to segmentation to classification, a comprehensive and logically clear solution is provided for blood vessel classification, and the accuracy and reliability of final blood vessel category prediction are ensured.

The present application relates to a kind of DSA coronary vessel segmentation deep learning method of edge and topological feature, comprising the following steps: obtaining DSA coronary angiography image dataset and corresponding coronary vessel label dataset;Data is input into deep learning network, the deep learning network is end-to-end U type deep learning network, include improved bidirectional Transformer module and multi-scale semantic fusion module, and encoder and decoder are connected by jump connection and multi-scale semantic fusion module;Coronary vessel segmentation model is trained in three stages using the composite loss function comprising Dice loss, Focal loss, edge loss and topological loss, the segmentation model of the training completion can be carried out complete segmentation to the coronary vessel in DSA coronary angiography picture;Using composite evaluation index, the overall segmentation accuracy of segmentation model, edge segmentation accuracy and the ability of keeping topological consistency are comprehensively evaluated.

A three-dimensional liver vessel segmentation model based on enhanced attention mechanism context bridging and its establishment method include: an input layer for receiving input three-dimensional liver medical CT image data to obtain image features; first to fourth image overlapping feature encoding layers for performing overlapping feature encoding operations on low-dimensional image features; first to fourth enhanced attention mechanism downsampling layers, which are alternately connected with the first to fourth image overlapping feature encoding layers in sequence, for feature extraction and generating four different-scale image features; first to fourth enhanced attention mechanism context bridging layers connected in series, for fusing multi-scale image features and further extracting features to obtain the association of local and global information of multi-scale image features; first to fourth image block expansion layers, which are used to re-divide high-dimensional image features into lower-dimensional image features while increasing the width, height, and depth of the image features; first to fourth enhanced attention mechanism upsampling layers, which are alternately connected with the first to fourth image block expansion layers in sequence, for recursively splicing and further extracting features from image features of different scales to obtain final image features; and an output layer, which is used to calculate the final image features and generate three-dimensional liver vessel segmentation results. The present invention can achieve accurate three-dimensional liver vessel segmentation and assist doctors in diagnosing diseases such as liver vessels and tumors.

The present invention belongs to the field of medical image processing technology and discloses an ASL image processing system, device, and terminal for cerebral artery stenosis and occlusion. The ASL image processing system for cerebral artery stenosis and occlusion comprises: a scanning parameter acquisition module, an image acquisition module, a central control module, a data post-processing module, an image pre-processing module, a CBF image generation module, a CBF image processing module, a vascular region demarcation module, a vascular segmentation module, a three-dimensional reconstruction module, a parameter calculation module, a data analysis module, and a display module. The present invention eliminates data errors caused by initial machine operation instability by deleting the earliest scan data. Simultaneously, the scan data is corrected and registered to avoid artifacts caused by user movement during processing, thereby improving image reliability and data analysis accuracy.

This invention discloses a method and device for cerebral artery segmentation and classification based on MRI images, belonging to the field of medical image processing. The method uses a U-Net network as its basic architecture, introducing a Swing Transformer module in the encoder to achieve hierarchical feature extraction. First, the 3D MRI image is preprocessed; then, multi-scale features are extracted through a multi-level Swing Transformer encoding stage, and a shift-window attention mechanism is combined to enhance global context awareness; next, a graph structure is constructed to fuse and enhance multi-level features to preserve vascular topology; finally, a convolutional decoder upsamples layer by layer and performs skip connections with the graph fused features to achieve accurate reconstruction and classification of vascular structures. This invention effectively integrates the global modeling capabilities of the Transformer with the local detail extraction advantages of convolutional networks, improving the accuracy and structural integrity of vascular segmentation and classification.

This application discloses a two-stage segmentation method for vascular structures in medical CT images and related devices. The method includes: acquiring a medical CT image and its coarse segmentation image; performing vascular morphology interpolation processing on the coarse segmentation image and performing HU value similarity determination to generate first annotation data and HU value similarity mapping data; performing vascular connectivity determination on the HU value similarity mapping data, extracting conditional labels, and generating second annotation data; and using a boundary erosion-based vascular extraction method to extract the target area in the repaired and compensated data, ultimately obtaining the target segmentation result. By combining HU value determination, vascular connectivity analysis, and boundary erosion technology, the present invention improves the accuracy and connectivity of vascular segmentation and is suitable for the fine extraction of complex vascular structures.

The application discloses a cerebral vascular segmentation method and device based on physical guidance and pyramid vision Transformer, relates to the field of medical image data, and comprises the following steps: constructing a cerebral vascular segmentation model and using a loss function in training, the loss function comprising a boundary intersection-over-union loss, a focal Tversky loss and a Dice loss; obtaining an optical coherence tomography image of a brain to be processed and inputting the image into the trained cerebral vascular segmentation model, and first passing through an encoder module of the pyramid vision Transformer, wherein output features of a first Transformer encoding layer are input into a radial intensity module to obtain radial enhancement features; output features of a second, a third and a fourth Transformer encoding layer are input into a deformable cross-scale fusion module to obtain enhanced fusion features; and the radial enhancement features and the enhanced fusion features are input into a boundary perception attention module to obtain corresponding cerebral vascular prediction segmentation masks and cerebral vascular prediction segmentation images. The application solves the problems of low segmentation accuracy and boundary precision in the prior art.

The invention discloses a method and equipment for segmenting an aneurysm and a storage medium. The method comprises the following steps: generating a blood vessel map structure according to a blood vessel segmentation mask, wherein the blood vessel map structure at least comprises leaf nodes containing radius attributes; determining an initial path node in the aneurysm according to the positioning point; screening target paths from the initial path node to the leaf node, and sorting the target paths according to an average radius to form a path pair; restoring the path passing through the aneurysm based on the path pair to obtain a restored path; and segmenting the aneurysm based on the reduction path to obtain an aneurysm segmentation result. By means of the scheme, stable segmentation of the large aneurysm can be guaranteed, the peripheral blood vessel condition of the aneurysm can be stably obtained, and the accuracy and reliability of aneurysm segmentation are improved.

This invention provides a method and apparatus for identifying retinal vein beads. One embodiment of the method includes: first, performing vascular segmentation processing on a target fundus image to output segmented vein vessels; wherein, the segmented vein vessels include main vessels and branch vessels; second, generating a diameter feature map based on the diameter feature sequence corresponding to the main vessels and / or the branch vessels; finally, if the graphic features of the diameter feature map meet preset conditions, determining that retinal vein beads exist on the segmented vein vessels. Therefore, this embodiment of the method can automatically identify retinal vein beads based on fundus images, helping to improve the accuracy of retinal vein bead identification, providing a reference for disease diagnosis and treatment, and contributing to improving the scientificity and accuracy of clinical decision-making.

Provided in the present invention are a vascular stenosis rate prediction method, prediction model and prediction system. The method comprises: acquiring an angiographic image, and performing vascular segmentation processing on the angiographic image to obtain a vascular segmentation map; and then using the vascular segmentation map as an input of a pre-trained deep learning-based vascular stenosis rate prediction model, and outputting a vascular stenosis rate distribution map after performing prediction by means of the prediction model, the vascular stenosis rate distribution map comprising a plurality of first pixels. By combining deep learning technology with clinical medicine, the prediction model, method and system accurately calculate the conditions of vascular stenosis by means of the deep learning technology, can be adapted to medical images of varying quality, have fast prediction speed and accurate and consistent prediction results, conform to diagnostic norms and standards of doctors, and solve the disadvantage of poor interpretability of deep learning; and the vascular stenosis rate prediction method has high accuracy.

The invention discloses a retina image global registration method based on style migration and a key point detection and description network. The method comprises the following steps: step 1, processing a multi-modal retina image into a blood vessel segmentation mask by using a style migration network; 2, extracting an initial key point set of the blood vessel segmentation mask by using a key point detection and description network and a non-maximum suppression operation; 3, calculating an initial key point matching set of the two pictures based on the distance between the feature descriptors; and step 4, carrying out transformation matrix estimation and outlier rejection based on a minimum median mean method. According to the method, retina images of different modalities are unified into a blood vessel segmentation mask through a style migration network, and global transformation matrix estimation is performed by using a feature-based method, so that the difficulty of feature detection, description and matching caused by different image modalities is avoided, and high-precision global registration of the multi-modal retina images is realized.

The invention belongs to the field of intelligent medical treatment, and particularly relates to a construction method and equipment of an intractable mycoplasma pneumonia prediction model. The method comprises the following steps: acquiring a data set of baseline time and a follow-up time label of a mycoplasma pneumoniae patient; inputting the image data set into a pulmonary blood vessel segmentation model to obtain a pulmonary blood vessel network, obtaining a total pulmonary blood vessel volume based on the pulmonary blood vessel network, and obtaining blood vessel volumes of different cross section areas based on the blood vessel cross section areas in the pulmonary blood vessel network; and inputting the ratio of the blood vessel volume of different cross section areas to the total pulmonary blood vessel volume into a machine learning model to obtain a prediction label, and iteratively optimizing the machine learning model based on the difference between the prediction label and the follow-up time label to obtain an intractable mycoplasma pneumonia prediction model. According to the method, the lung CT image is segmented to obtain the blood vessel network graph, the percentage of the volume of blood vessels with different thicknesses in the total lung blood vessel volume is obtained through quantification, and the refractory pneumonia prediction model is constructed based on an innovative feature extraction mode.

This application discloses a method, device, and storage medium for training a segmentation model for segmenting vascular segments. The method includes: acquiring multimodal imaging data, including digital subtraction angiography data, CT angiography data, and magnetic resonance angiography data; performing vascular segmentation and extracting vascular surfaces based on the multimodal imaging data to obtain vascular surface data; annotating positioning points and vascular segments on the vascular surface data, and inputting the annotated vascular surface data into a positioning model for training; calculating distance features on the vascular surface data based on the positioning point information; and inputting the vascular surface data, distance features, and vascular segment annotations into the segmentation model to perform vascular segmentation to obtain vascular segments, thereby training the segmentation model for segmenting vascular segments. Using the solution of this application, the segmentation accuracy of the segmentation model can be improved, resulting in more accurate vascular segmentation results.

The application discloses a method and device for training a detection model for detecting vascular adhesion and a storage medium. The method comprises: collecting multi-modal image data, wherein the multi-modal image data comprises digital subtraction angiography data, CT angiography data and magnetic resonance angiography data; performing vascular segmentation based on the multi-modal image data to obtain a graph structure of the blood vessels; extracting a graph structure feature of a single blood vessel and a target blood vessel feature according to the graph structure, and labeling whether the blood vessels have passed through adhesion blood vessels; constructing training data based on the graph structure feature of the single blood vessel, the target blood vessel feature and the labeling result; inputting the training data into a detection model to perform vascular adhesion detection to obtain a detection result, so as to train the detection model for detecting vascular adhesion. The scheme disclosed by the application can improve the accurate positioning and efficient detection of vascular adhesion.

This application discloses a method, device, medium, and product for determining a cerebral vascular aging index, relating to the field of image data processing. The method comprises: extracting the central axis of a cerebral vascular segmentation image using Voronoi covariance, then determining the tangent and normal plane corresponding to each voxel on the central axis using the λ-maximum segment tangent method; fitting a maximum circle in the corresponding normal plane for each voxel and determining the corresponding diameter; performing a three-dimensional reconstruction of the tubular tree structure based on the central axis and diameter; extracting multi-level standardized imaging features from the three-dimensionally reconstructed image, and inputting these features into a cerebral vascular biological age prediction model to obtain a cerebral vascular biological age prediction result; and calculating the cerebral vascular aging index by combining the cerebral vascular biological age prediction result with the user's actual age. This application enables quantitative and accurate assessment of cerebral vascular aging.

The invention discloses an nnUNet zebra fish juvenile fish whole-brain blood vessel system segmentation method based on autonomous data set training, and relates to the technical field of high-resolution imaging technology, image processing and medical image segmentation. According to the method, high-resolution whole-brain three-dimensional blood vessel image data is obtained by fully utilizing the transparency and fluorescence labeling advantages of a zebra fish living body; and a high-quality segmentation truth value database is constructed through semi-automatic segmentation and manual correction, and an nnU-Net deep learning model is adopted for training, so that three-dimensional automatic segmentation of zebra fish cerebrovascular system signals is realized. According to the method, the automation degree and precision of image segmentation are remarkably improved, and the problems of low efficiency, high manual dependency, poor repeatability and the like of traditional cerebral vessel segmentation are effectively solved. The method is suitable for large-scale high-throughput data processing, can provide an efficient and standardized image processing scheme for zebra fish cerebrovascular development mechanism and cerebrovascular disease model research, and has a wide application prospect.

The present application provides a coronary artery segmentation method, apparatus, electronic device, and storage medium, comprising: extracting the centerline of an acquired unilateral coronary angiography image to be segmented to obtain the centerline of the coronary artery trunk; determining the target coronary artery side to which the coronary angiography image belongs based on the centerline of the coronary artery trunk; performing channel splicing on the coronary angiography image and its two preceding and following frames to determine a first spliced ​​image; performing gradient domain processing on the coronary angiography image to obtain its gradient domain mapping, and performing channel splicing to determine a second image; inputting the first spliced ​​image into a unilateral coronary artery segmentation model corresponding to the target coronary artery side to obtain a first coronary artery image; inputting the second spliced ​​image into a hybrid coronary artery segmentation model to obtain a second coronary artery image; and fusing the first coronary artery image and the second coronary artery image to obtain a target coronary artery vessel image. Thus, the technical solution of the present application improves the accuracy of the coronary artery vessel segmentation results.