SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
This work addresses the problem of slow and computationally intensive segmentation for medical imaging researchers and practitioners, offering an incremental improvement over existing methods.
The paper tackles the computational challenge of Transformer-based models for 3D medical image segmentation by introducing SegMamba, a model based on Mamba (a State Space Model) that captures long-range dependencies efficiently, achieving superior processing speed on volume features at 64×64×64 resolution as demonstrated on the BraTS2023 dataset.
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