CS-MUNet: A Channel-Spatial Dual-Stream Mamba Network for Multi-Organ Segmentation

TL;DR

CS-MUNet introduces a novel dual-stream Mamba network that enhances multi-organ segmentation by explicitly modeling cross-channel semantic collaboration and boundary-aware feature fusion, achieving superior performance on benchmark datasets.

Contribution

The paper presents CS-MUNet, a new architecture with boundary-aware modules and channel aggregation, addressing limitations of previous Mamba-based methods in multi-organ segmentation.

Findings

Outperforms state-of-the-art methods on public benchmarks

Effectively models cross-channel semantic collaboration

Enhances boundary-aware feature fusion

Abstract

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.