Medical image segmentation,Mamba,CNN,Attention Mechanism
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Journal of Bionic Engineering ›› 2025, Vol. 22 ›› Issue (4): 2135-2150.doi: 10.1007/s42235-025-00711-x

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DGFE-Mamba: Mamba-Based 2D Image Segmentation Network

Junding Sun1;Kaixin Chen1;Shuihua Wang2;Yudong Zhang1,3,4;Zhaozhao Xu1;Xiaosheng Wu1;Chaosheng Tang1 #br#

  

  1. 1 School of Computer Science and Technology, HenanPolytechnic University, Jiaozuo 454000, Henan, China 2 Department of Biological Sciences, Xi’an Jiaotong-LiverpoolUniversity, Suzhou 215123, Jiangsu, China
    3 School of Computing and Mathematical Sciences, Universityof Leicester, Leicester LE1 7RH, UK
    4 Department of Information Systems, Faculty of Computingand Information Technology, King Abdulaziz University,Jeddah 21589, Saudi Arabia

  • Online:2025-06-19 Published:2025-08-31
  • Contact: Junding Sun;Yudong Zhang E-mail:sunjd@hpu.edu.cn;yudongzhang@ieee.org
  • About author:Junding Sun1;Kaixin Chen1;Shuihua Wang2;Yudong Zhang1,3,4;Zhaozhao Xu1;Xiaosheng Wu1;Chaosheng Tang1

Abstract: In the field of medical image processing, combining global and local relationship modeling constitutes an effective strategy for precise segmentation. Prior research has established the validity of Convolutional Neural Networks (CNN) in modeling local relationships. Conversely, Transformers have demonstrated their capability to effectively capture global contextual information. However, when utilized to address CNNs’ limitations in modeling global relationships, Transformers are hindered by substantial computational complexity. To address this issue, we introduce Mamba, a State-Space Model (SSM) that exhibits exceptional proficiency in modeling long-range dependencies in sequential data. Given Mamba’s demonstrated potential in 2D medical image segmentation in previous studies, we have designed a Dual-encoder Globallocal Feature Extraction Network based on Mamba, termed DGFE-Mamba, to accurately capture and fuse long-range dependencies and local dependencies within multi-scale features. Compared to Transformer-based methods, the DGFEMamba model excels in comprehensive feature modeling and demonstrates significantly improved segmentation accuracy. To validate the effectiveness and practicality of DGFE-Mamba, we conducted tests on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset, the Synapse multi-organ CT abdominal segmentation dataset, and the Colorectal Cancer Clinic (CVC-ClinicDB) dataset. The results showed that DGFE-Mamba achieved Dice coefficients of 92.20, 83.67, and 94.13, respectively. These findings comprehensively validate the effectiveness and practicality of the proposed DGFE-Mamba architecture.

Key words: Medical image segmentation')">Medical image segmentation, Mamba, CNN, Attention Mechanism