内蒙古民族大学学报（自然科学版）2026，Vol.41Issue(1)：50-58,9.DOI:10.14045/j.cnki.15-1220.2026.01.007
基于双路编码器融合与注意力门控的脑卒中图像分割网络
Stroke Image Segmentation Network Based on Dual-path Encoder Fusion and Attention Gating Mechanism
摘要
Abstract
Ischemic stroke lesions in 3D magnetic resonance imaging(MRI)are characterized by small volume,heterogeneous morphology,and indistinct boundaries,making automatic segmentation highly challenging.To enhance global semantic consistency and local detail representation,a 3D medical image segmentation network,DFAG-Net,is developed based on dual-encoder fusion and attentiongating mechanisms.The network integrates a Swin Transformer encoder with a lightweight 3D convolutional encoder.Cross-branch and multi-scale semantic interaction are achieved through an Encoder Fusion Module(EFM),while selective spatial filtering across hierarchical features is performed using an Attention-Gated Skip Module(AGSM).Experiments conducted on the ISLES 2022 and ATLAS v2.0 stroke datasets demonstrate that DFAG-Net surpasses multiple mainstream segmentation models in Dice coefficient,HD95,precision,and recall.Specifically,the Dice on ISLES 2022 and ATLAS v2.0 reached 74.50%and 56.80%,respectively.The results indicate that the network exhibits strong stability and adaptability in 3D stroke lesion segmentation,providing effective technical support for quantitative analysis and intelligent assisted diagnosis of ischemic stroke lesions.关键词
深度学习/医学图像分割/脑卒中/注意力门控机制Key words
deep learning/medical image segmentation/stroke/attention gating mechanism分类
信息技术与安全科学引用本文复制引用
赵云伟,白青海,廉洁,谭克强,姜明洋..基于双路编码器融合与注意力门控的脑卒中图像分割网络[J].内蒙古民族大学学报（自然科学版）,2026,41(1):50-58,9.基金项目
国家自然科学基金项目(62162049) （62162049）
内蒙古自治区重点研发项目(2025SSYFDZ0411) （2025SSYFDZ0411）
