  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
