10 results about "Vascular segmentation" patented technology

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.

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 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.

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.

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.

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 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.