ASM-UNet：结合群体共性与个体差异的适应性扫描Mamba用于细粒度分割
ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation
AI总结：
提出ASM-UNet架构，通过结合群体共性与个体差异生成适应性扫描分数以动态指导扫描顺序，解决了细粒度分割中固定扫描顺序的局限，在多个数据集上的CGS和FGS任务中均取得优越性能。
摘要：
精确的病灶切除依赖于准确识别细粒度的解剖结构。尽管许多粗粒度分割（CGS）方法在大规模分割（例如器官）中取得了成功，但在需要细粒度分割（FGS）的临床场景中表现不足，这仍然具有挑战性，因为小规模解剖结构中频繁的个体差异。尽管最近基于Mamba的模型推进了医学图像分割，但它们通常依赖固定的手动定义扫描顺序，这限制了它们对FGS中个体差异的适应性。为此，我们提出ASM-UNet，一种用于FGS的新型基于Mamba的架构。它引入适应性扫描分数来动态指导扫描顺序，该分数通过结合群体级共性和个体级差异生成。在两个公开数据集（ACDC和Synapse）以及一个新提出的具有挑战性的胆道 tract 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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
