ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and
  Individual Variations for Fine-Grained Segmentation
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.
689b4d2dfab6fdd2e52ac6ec
Bo Wang
689b4d2dfab6fdd2e52ac6ed
Mengyuan Xu
689b4d2dfab6fdd2e52ac6ee
Yue Yan
689b4d2dfab6fdd2e52ac6ef
Yuqun Yang
claimed_verified
2025-08-14T13:39:08.493Z
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/avatars/056b2b4496797989be2470ed146c1d09.svg
yang
user
Yuqunyang
689b4d2dfab6fdd2e52ac6f0
Kechen Shu
689b4d2dfab6fdd2e52ac6f1
Wei Ping
689b4d2dfab6fdd2e52ac6f2
Xu Tang
689b4d2dfab6fdd2e52ac6f3
Wei Jiang
689b4d2dfab6fdd2e52ac6f4
Zheng You
ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and
  Individual Variations for Fine-Grained Segmentation
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.
ASM-UNet, a Mamba-based architecture with adaptive scan scores, enhances fine-grained segmentation by dynamically adjusting scanning orders to accommodate individual anatomical variations.
Mamba-based models adaptive scan scores fine-grained segmentation coarse-grained segmentation ACDC Synapse BTMS