SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation

• URL: http://arxiv.org/abs/2401.13560v4
• Date: Sun, 15 Sep 2024 13:26:45 GMT
• Title: SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
• Authors: Zhaohu Xing, Tian Ye, Yijun Yang, Guang Liu, Lei Zhu,
• Abstract summary: We introduce SegMamba, a novel 3D medical image textbfSegmentation textbfMamba model. SegMamba excels in whole volume feature modeling from a state space model standpoint. Experiments on the BraTS2023 dataset demonstrate the effectiveness and efficiency of our SegMamba.
• Score: 16.476244833079182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Transformer architecture has shown a remarkable ability in modeling global relationships. However, it poses a significant computational challenge when processing high-dimensional medical images. This hinders its development and widespread adoption in this task. Mamba, as a State Space Model (SSM), recently emerged as a notable manner for long-range dependencies in sequential modeling, excelling in natural language processing filed with its remarkable memory efficiency and computational speed. Inspired by its success, we introduce SegMamba, a novel 3D medical image \textbf{Seg}mentation \textbf{Mamba} model, designed to effectively capture long-range dependencies within whole volume features at every scale. Our SegMamba, in contrast to Transformer-based methods, excels in whole volume feature modeling from a state space model standpoint, maintaining superior processing speed, even with volume features at a resolution of {$64\times 64\times 64$}. Comprehensive experiments on the BraTS2023 dataset demonstrate the effectiveness and efficiency of our SegMamba. The code for SegMamba is available at: https://github.com/ge-xing/SegMamba

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