ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and
  Individual Variations for Fine-Grained Segmentation
Authors: name: Bo Wang, name: Mengyuan Xu, name: Yue Yan, name: Yuqun Yang, name: Kechen Shu, name: Wei Ping, name: Xu Tang, name: Wei Jiang, name: Zheng You

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.