OSHMAMBA: One-Scan Hybrid Mamba with Dynamic Chunking for 3D Medical Image Segmentation

Le résumé fourni par la source

Segmenting small lesions in 3D medical images requires capturing long-range dependencies, a task for which Transformers are effective but computationally expensive. While the Mamba architecture offers a linearly efficient alternative, it can lose critical spatial information. We propose OSHMamba, a novel U-shaped hybrid network that effectively combines the efficiency of Mamba with the accuracy of Transformer. Its core component, the Dynamic Block Hybrid Mamba (DCHMamba) encoder, uses Mamba for a full-volume scan and reserves the powerful Transformer attention for dynamically identified key regions, optimizing computational allocation. A Dual-Stream Feature Enhancement (DSFE) module further refines segmentation boundaries by integrating detail-rich features. Experiments on four public datasets confirm that Oshmamba achieves stable and accurate performance, especially for small, complex lesions.

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