Computer Science

Computer Science ›› 2026, Vol. 53 ›› Issue (2): 264-272.doi: 10.11896/jsjkx.250300137

• Computer Grapnics & Multimedia • Previous Articles     Next Articles

Boundary-focused Multi-scale Feature Fusion Network for Stroke Lesion Segmentation

LIU Chenhong1, LI Fenglian1, YANG Jia2, WANG Suzhe1, CHEN Guijun1   

  1. 1 College of Electronic Information Engineering,Taiyuan University of Technology,Taiyuan 030024,China
    2 R&D Department,China Academy of Launch Vehicle Technology,Beijing 100076,China
  • Received:2025-03-25 Revised:2025-05-22 Published:2026-02-10
  • About author:LIU Chenhong,born in 2000,postgra-duate,is a member of CCF(No.Z5771G).Her main research interest is medical image segmentation.
    LI Fenglian,born in 1972,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.65512S).Her main research interest is medical signal processing and analysis.
  • Supported by:
    National Natural Science Foundation of China(62171307) and Natural Science Foundation of Shanxi Province,China(202103021224113).

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Abstract: Computer-aided diagnosis helps clinicians locate stroke-affected brain regions,improving diagnostic and therapeutic efficiency.Currently,the boundaries between stroke lesions and healthy tissues in medical images are often unclear,and most exis-ting deep learning-based segmentation methods lack effectiveness in identifying small-sized lesions and handling blurred boundaries.To address this,the boundary-aware multi-scale feature integration network(BAMFNet) is proposed for more accurate stroke lesion segmentation.In BAMFNet,the multi-scale feature extraction module combines convolutional neural networks and Transformers to capture local and global features at multiple scales and uses involution to reduce information redundancy.The boundary enhancement and fusion module strengthens boundary-region features during fusion and integrates a multi-level information interaction mechanism.This enhances the boundary feature representation and combines deep and shallow features effectively.Experiments on the ATLAS v1.2,ATLAS v2.0 and ISLES 2022 stroke datasets show BAMFNet achieves Dice similarity coefficients of 62.93%,61.79%,and 86.66% respectively,outperforming other methods.

Key words: Deep learning, Lesion segmentation, Multi-scale feature fusion, Boundary enhancement, Transformer

CLC Number: 

  • TP391
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