Mamba在三维体积医学图像分割中的综合分析
A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation
AI总结：
本研究全面分析Mamba在三维医学图像分割中的能力，提出多尺度Mamba块和Tri-scan策略，证明其优于Transformer及现有模型，兼具高精度与计算效率。
摘要：
Mamba凭借其选择性状态空间模型（SSMs），在长距离依赖建模方面提供了比Transformer更高效的计算解决方案。然而，关于其在三维高分辨率医学图像分割中的有效性仍存在争议。在本研究中，我们通过解决三个关键问题对Mamba在三维医学图像分割中的能力进行了全面调查：Mamba能否取代Transformer？它能否提升多尺度表示学习？复杂的扫描策略是否是释放其全部潜力的必要条件？我们在三个大型公开基准数据集——AMOS、TotalSegmentator和BraTS上评估了Mamba的性能。我们的研究结果表明，基于U形Mamba网络的UlikeMamba始终优于基于U形Transformer网络的UlikeTrans，尤其是在通过自定义设计的3D深度卷积增强后，准确性和计算效率均得到提升。此外，我们提出的多尺度Mamba模块在捕获细粒度细节和全局上下文方面表现出优越性能，尤其是在复杂分割任务中，超越了基于Transformer的对应方法。我们还批判性地评估了复杂扫描策略，发现简单方法通常已足够，而我们的Tri-scan方法在最具挑战性的场景中带来了显著优势。通过整合这些进展，我们提出了一种新的三维医学图像分割网络，将Mamba定位为一种变革性力量，其性能优于nnUNet、CoTr和U-Mamba等领先模型，在提供竞争性准确性的同时具有卓越的计算效率。本研究为Mamba的独特优势提供了关键见解，为更高效、更准确的三维医学成像方法铺平了道路。
英文摘要：
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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