提出双路径Mamba模型，提升3D医学图像分割精度与边界清晰度。
HybridMamba: A Dual-domain Mamba for 3D Medical Image Segmentation
- 融合轴向扫描与自适应局部路径，平衡全局与局部特征
- 在多中心肺癌CT与MRI数据集上达到最佳分割效果
- 适合需要高精度边界识别的医学图像分析任务
在3D生物医学图像分割领域，Mamba模型因克服了传统CNN在建模长程依赖上的缺陷，并缓解了基于Transformer框架处理高分辨率医学体数据时的计算开销问题，表现出优越性能。然而，过度强调全局上下文建模可能削弱关键局部结构信息，导致分割结果出现边界模糊和区域失真。为此，本文提出HybridMamba，采用双互补机制：1）特征扫描策略，通过逐层整合轴向遍历与局部自适应路径表征，协调局部与全局表示关系；2）结合空间-频率分析的门控模块，实现全面的上下文建模。此外，我们构建了一个多中心肺癌CT数据集。在MRI与CT数据集上的实验表明，HybridMamba显著优于当前最优方法。
原文摘要 · Abstract (English)
In the domain of 3D biomedical image segmentation, Mamba exhibits the superior performance for it addresses the limitations in modeling long-range dependencies inherent to CNNs and mitigates the abundant computational overhead associated with Transformer-based frameworks when processing high-resolution medical volumes. However, attaching undue importance to global context modeling may inadvertently compromise critical local structural information, thus leading to boundary ambiguity and regional distortion in segmentation outputs. Therefore, we propose the HybridMamba, an architecture employing dual complementary mechanisms: 1) a feature scanning strategy that progressively integrates representations both axial-traversal and local-adaptive pathways to harmonize the relationship between local and global representations, and 2) a gated module combining spatial-frequency analysis for comprehensive contextual modeling. Besides, we collect a multi-center CT dataset related to lung cancer. Experiments on MRI and CT datasets demonstrate that HybridMamba significantly outperforms the state-of-the-art methods in 3D medical image segmentation.
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