A method and apparatus for cerebral artery segmentation classification based on MRI images

By constructing an end-to-end U-shaped encoder-decoder network and a Swing Transformer module, the problems of long imaging time, noise interference, and imbalance in the proportion of blood vessel segments in MRI images for cerebral artery segment classification were solved, achieving accurate cerebral artery segment classification and improved diagnostic efficiency.

CN121686115BActive Publication Date: 2026-06-30ZHEJIANG UNIV OF TECH +2
2 Cites 0 Cited by

Patent Information

Application Number
CN202610179027.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-02-09
Publication Date
2026-06-30
Estimated Expiration
2046-02-09

AI Technical Summary

Technical Problem

Existing MRI image processing techniques suffer from problems such as long imaging time, uneven image quality, noise interference, and unbalanced proportions of vascular segments in cerebral artery segmentation classification. These issues lead to segmentation errors and decreased accuracy, making it difficult to achieve precise cerebral artery segmentation classification.

Method used

An end-to-end U-shaped encoder-decoder network based on MRI images was constructed. The Swin Transformer module was used for feature extraction, and combined with a multi-scale feature fusion module and data augmentation technology, accurate vascular segment classification was achieved through image preprocessing and multi-level feature reconstruction.

Benefits of technology

It significantly improves the model's robustness to noise and the accuracy of blood vessel segmentation, maintains the integrity of blood vessel topology, and enhances classification performance and diagnostic efficiency.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

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.
Need to check novelty before this filing date? Find Prior Art