A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation

• URL: http://arxiv.org/abs/2503.19308v1
• Date: Tue, 25 Mar 2025 03:14:31 GMT
• Title: A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation
• Authors: Chaohan Wang, Yutong Xie, Qi Chen, Yuyin Zhou, Qi Wu,
• Abstract summary: We present a comprehensive investigation into Mamba's capabilities in 3D medical image segmentation.<n>We evaluate Mamba's performance across three large public benchmarks-AMOS, TotalSegmentator, and BraTS.<n>Our findings reveal that UlikeMamba, a U-shape Mamba-based network, consistently surpasses UlikeTrans, a U-shape Transformer-based network.
• Score: 32.79661488280031
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mamba, with its selective State Space Models (SSMs), offers a more computationally efficient solution than Transformers for long-range dependency modeling. However, there is still a debate about its effectiveness in high-resolution 3D medical image segmentation. In this study, we present a comprehensive investigation into Mamba's capabilities in 3D medical image segmentation by tackling three pivotal questions: Can Mamba replace Transformers? Can it elevate multi-scale representation learning? Is complex scanning necessary to unlock its full potential? We evaluate Mamba's performance across three large public benchmarks-AMOS, TotalSegmentator, and BraTS. Our findings reveal that UlikeMamba, a U-shape Mamba-based network, consistently surpasses UlikeTrans, a U-shape Transformer-based network, particularly when enhanced with custom-designed 3D depthwise convolutions, boosting accuracy and computational efficiency. Further, our proposed multi-scale Mamba block demonstrates superior performance in capturing both fine-grained details and global context, especially in complex segmentation tasks, surpassing Transformer-based counterparts. We also critically assess complex scanning strategies, finding that simpler methods often suffice, while our Tri-scan approach delivers notable advantages in the most challenging scenarios. By integrating these advancements, we introduce a new network for 3D medical image segmentation, positioning Mamba as a transformative force that outperforms leading models such as nnUNet, CoTr, and U-Mamba, offering competitive accuracy with superior computational efficiency. This study provides key insights into Mamba's unique advantages, paving the way for more efficient and accurate approaches to 3D medical imaging.

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