arXiv 2504.175152025-04-24cs.CV

Mamba-Sea：一种基于Mamba的全局到局部序列增强框架，用于可泛化的医学图像分割

Mamba-Sea: A Mamba-based Framework with Global-to-Local Sequence Augmentation for Generalizable Medical Image Segmentation

Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；National Institute of Healthcare Data Science（健康数据科学国家研究院）；State Key Laboratory for Novel Software Technology（新型软件技术国家重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Key Lab of Computer Network and Information Integration（计算机网络与信息集成重点实验室）；School of Electrical and Information Engineering（电气与信息工程学院）；The University of Sydney（悉尼大学）

Zihan Cheng, Jintao Guo, Jian Zhang, Lei Qi, Luping Zhou, Yinghuan Shi, Yang Gao

备注：: Accepted by IEEE TMI 2025. The code is available at https://github.com/orange-czh/Mamba-Sea

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AI总结：

本文提出Mamba-Sea，首个基于Mamba的医学图像分割域泛化框架，通过全局到局部序列增强提升泛化能力，在前列腺数据集上首次超过90%的Dice系数。

摘要：

针对医学图像分割中的分布偏移问题，域泛化（DG）作为一种有前景的设置，旨在源域上训练模型，使其能够泛化到未见过的目标域。现有的DG方法主要基于CNN或ViT架构。近年来，以Mamba为代表的高级状态空间模型在各种监督医学图像分割中显示出有希望的结果。Mamba的成功主要归功于其捕获长距离依赖的能力，同时保持与输入序列长度成线性的复杂度，使其成为CNN和ViT的有前景替代方案。受此成功启发，本文探索了Mamba架构在解决医学图像分割DG中分布偏移问题的潜力。具体而言，我们提出了一种新颖的基于Mamba的框架Mamba-Sea，该框架结合了全局到局部序列增强，以提高模型在域偏移问题下的泛化能力。我们的Mamba-Sea引入了一种全局增强机制，旨在模拟不同站点间外观的潜在变化，以抑制模型对域特定信息的学习。在局部层面，我们提出了一种沿输入序列的序列级增强，通过建模和重采样与域偏移相关的风格统计量，扰动随机连续子序列中令牌的风格。据我们所知，Mamba-Sea是首个探索Mamba在医学图像分割中泛化能力的工作，提供了一种先进且有前景的基于Mamba的架构，对域偏移具有强鲁棒性。值得注意的是，我们的方法首次在前列腺数据集上超过了90%的Dice系数，超过了之前最先进的88.61%。代码可在https://github.com/orange-czh/Mamba-Sea获取。

英文摘要：

To segment medical images with distribution shifts, domain generalization (DG) has emerged as a promising setting to train models on source domains that can generalize to unseen target domains. Existing DG methods are mainly based on CNN or ViT architectures. Recently, advanced state space models, represented by Mamba, have shown promising results in various supervised medical image segmentation. The success of Mamba is primarily owing to its ability to capture long-range dependencies while keeping linear complexity with input sequence length, making it a promising alternative to CNNs and ViTs. Inspired by the success, in the paper, we explore the potential of the Mamba architecture to address distribution shifts in DG for medical image segmentation. Specifically, we propose a novel Mamba-based framework, Mamba-Sea, incorporating global-to-local sequence augmentation to improve the model's generalizability under domain shift issues. Our Mamba-Sea introduces a global augmentation mechanism designed to simulate potential variations in appearance across different sites, aiming to suppress the model's learning of domain-specific information. At the local level, we propose a sequence-wise augmentation along input sequences, which perturbs the style of tokens within random continuous sub-sequences by modeling and resampling style statistics associated with domain shifts. To our best knowledge, Mamba-Sea is the first work to explore the generalization of Mamba for medical image segmentation, providing an advanced and promising Mamba-based architecture with strong robustness to domain shifts. Remarkably, our proposed method is the first to surpass a Dice coefficient of 90% on the Prostate dataset, which exceeds previous SOTA of 88.61%. The code is available at https://github.com/orange-czh/Mamba-Sea.

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