动态调整扫描顺序，提升细粒度医学图像分割精度
ASM-UNet: Adaptive Scan Mamba Integrating Group Commonalities and Individual Variations for Fine-Grained Segmentation
- 用群体共性与个体差异融合生成自适应扫描得分
- 在ACDC、Synapse和新数据集BTMS上均超越现有方法
- 适合需要精细解剖结构分割的临床医学场景
精准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）中表现良好，但在需要细粒度分割（FGS）的临床场景中仍面临挑战，主要因小尺度解剖结构存在频繁的个体差异。尽管近期基于Mamba的模型已推动医学图像分割进展，但其通常依赖固定的手动设定扫描顺序，难以适应FGS中的个体差异。为此，我们提出ASM-UNet，一种面向FGS的新型Mamba架构。该模型引入自适应扫描得分，通过融合群体层面共性与个体层面差异动态引导扫描顺序。在两个公开数据集（ACDC和Synapse）及一个新提出的具有挑战性的胆道系统细粒度分割数据集BTMS上的实验表明，ASM-UNet在CGS与FGS任务中均取得更优性能。代码与数据集已开源：https://github.com/YqunYang/ASM-UNet。
原文摘要 · Abstract (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.
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