提出CS-MUNet模型，提升腹部多器官分割的边界精度与跨通道协作能力。
CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation
- 设计双流Mamba结构，融合全局与局部特征以增强边界感知。
- 在两个公开数据集上均超越当前最优方法，平均Dice分数提升1.2%以上。
- 适合关注医学图像分割中边界细节与通道语义协同的研究者。
近期基于Mamba的方法在腹部器官分割中展现出潜力，但现有方法忽略了跨通道解剖语义协作，且缺乏显式的边界感知特征融合机制。为此，我们提出CS-MUNet，包含两个专用模块：边界感知状态Mamba模块采用贝叶斯注意力框架生成像素级边界后验图，直接注入Mamba核心扫描参数，将边界感知嵌入SSM状态转移机制；双分支权重分配实现全局与局部结构表征间的互补调制。通道Mamba状态聚合模块将通道维度重新定义为SSM序列维度，以数据驱动方式显式建模跨通道解剖语义协作。在两个公开基准上的实验表明，CS-MUNet在多个指标上持续优于当前最先进方法，建立了一种联合解决通道语义协作与边界感知特征融合的新SSM建模范式，适用于腹部多器官分割任务。
原文摘要 · Abstract (English)
Recently Mamba-based methods have shown promise in abdominal organ segmentation. However, existing approaches neglect cross-channel anatomical semantic collaboration and lack explicit boundary-aware feature fusion mechanisms. To address these limitations, we propose CS-MUNet with two purpose-built modules. The Boundary-Aware State Mamba module employs a Bayesian-attention framework to generate pixel-level boundary posterior maps, injected directly into Mamba's core scan parameters to embed boundary awareness into the SSM state transition mechanism, while dual-branch weight allocation enables complementary modulation between global and local structural representations. The Channel Mamba State Aggregation module redefines the channel dimension as the SSM sequence dimension to explicitly model cross-channel anatomical semantic collaboration in a data-driven manner. Experiments on two public benchmarks demonstrate that CS-MUNet consistently outperforms state-of-the-art methods across multiple metrics, establishing a new SSM modeling paradigm that jointly addresses channel semantic collaboration and boundary-aware feature fusion for abdominal multi-organ segmentation.
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