ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation

TL;DR

ASM-UNet is a novel adaptive Mamba-based segmentation model that dynamically guides scanning order by integrating group commonalities and individual variations, significantly improving fine-grained medical image segmentation accuracy.

Contribution

The paper introduces ASM-UNet, which adaptively guides scanning in Mamba-based models for fine-grained segmentation, addressing limitations of fixed scanning orders in existing methods.

Findings

Achieves superior performance on multiple datasets

Effectively captures individual anatomical variations

Outperforms existing coarse and fine-grained segmentation methods

Abstract

Precise lesion resection depends on accurately identifying fine-grained anatomical structures. While many coarse-grained segmentation (CGS) methods have been successful in large-scale segmentation (e.g., organs), they fall short in clinical scenarios requiring fine-grained segmentation (FGS), which remains challenging due to frequent individual variations in small-scale anatomical structures. Although recent Mamba-based models have advanced medical image segmentation, they often rely on fixed manually-defined scanning orders, which limit their adaptability to individual variations in FGS. To address this, we propose ASM-UNet, a novel Mamba-based architecture for FGS. It introduces adaptive scan scores to dynamically guide the scanning order, generated by combining group-level commonalities and individual-level variations. Experiments on two public datasets (ACDC and Synapse) and a newly proposed challenging biliary tract FGS dataset, namely BTMS, demonstrate that ASM-UNet achieves superior performance in both CGS and FGS tasks. Our code and dataset are available at https://github.com/YqunYang/ASM-UNet.