文献类型：期刊文献

英文题名：MSD-KMamba: Bidirectional Spatial-Aware Multi-Modal 3D Brain Segmentation via Multi-scale Self-Distilled Fusion Strategy

作者：Tan, Dayu[1]; Zhang, Ziwei[1]; Su, Yansan[1]; Peng, Xin[2]; Dai, Yike[3]; Zheng, Chunhou[1]; Zhong, Weimin[2]

机构：[1] Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, 230601, China; [2] Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [3] Department of Orthopedic, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China

年份：2025

外文期刊名：arXiv

收录：EI(收录号：20250467420)

语种：英文

外文关键词：Brain - Computational complexity - Computational efficiency - Distillation - Extraction - Feature extraction - Medical image processing - Semantics

摘要：Numerous CNN-Transformer hybrid models rely on high-complexity global attention mechanisms to capture long-range dependencies, which introduces non-linear computational complexity and leads to significant resource consumption. Although knowledge distillation and sparse attention mechanisms can improve efficiency, they often fall short of delivering the high segmentation accuracy necessary for complex tasks. Balancing model performance with computational efficiency remains a critical challenge. In this work, we propose a novel 3D multimodal image segmentation framework, termed MSD-KMamba, which integrates bidirectional spatial perception with multi-scale self-distillation fusion strategy. The bidirectional spatial aware branch effectively captures long-range spatial context dependencies across brain regions, while also incorporating a powerful nonlinear feature extraction mechanism that further enhances the model’s ability to learn complex and heterogeneous patterns. In addition, the proposed multi-scale self-distilled fusion strategy strengthens hierarchical feature representations and improves the transfer of semantic information at different resolution levels. By jointly leveraging the bidirectional spatial perception branch and the multi-scale self-distilled fusion strategy, our framework effectively mitigates the bottleneck of quadratic computational complexity in volumetric segmentation, while simultaneously addressing the limitation of insufficient global perception. Extensive experiments on multiple standard benchmark datasets demonstrate that MSD-KMamba consistently outperforms state-of-the-art methods in segmentation accuracy, robustness, and generalization, while maintaining high computational efficiency and favorable scalability. The source code of MSD-KMamba is publicly available at https://github.com/daimao-zhang/MSDKMamba. Copyright ? 2025, The Authors. All rights reserved.