ASM-UNet：自适应扫描Mamba模型，融合群体共性与个体差异，实现精细分割

摘要

精准的病灶切除依赖于对细粒度解剖结构的准确识别。尽管许多粗粒度分割（CGS）方法在大规模分割（如器官）中取得了成功，但在需要细粒度分割（FGS）的临床场景中却表现不足，这由于小尺度解剖结构频繁的个体差异而仍具挑战性。虽然近期基于Mamba的模型在医学图像分割领域取得了进展，但它们往往依赖于固定的人工定义扫描顺序，这限制了其对FGS中个体差异的适应性。为此，我们提出了ASM-UNet，一种新颖的基于Mamba的FGS架构。它引入了自适应扫描评分，通过结合群体共性和个体差异动态指导扫描顺序。在两个公开数据集（ACDC和Synapse）以及新提出的具有挑战性的胆道系统FGS数据集（即BTMS）上的实验表明，ASM-UNet在CGS和FGS任务中均实现了卓越性能。我们的代码和数据集可在https://github.com/YqunYang/ASM-UNet获取。

English

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：自适应扫描Mamba模型，融合群体共性与个体差异，实现精细分割

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

摘要

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