ASM-UNet：整合群體共性與個體差異的自適應掃描Mamba網絡，用於精細分割

摘要

精確的病灶切除依賴於對細粒度解剖結構的準確識別。雖然許多粗粒度分割（CGS）方法在大規模分割（如器官）中取得了成功，但在需要細粒度分割（FGS）的臨床場景中，它們卻表現不足，這是由於小尺度解剖結構中頻繁的個體差異所帶來的挑戰。儘管基於Mamba的最新模型在醫學影像分割方面取得了進展，但它們通常依賴於固定的人工定義掃描順序，這限制了它們在FGS中對個體差異的適應能力。為解決這一問題，我們提出了ASM-UNet，一種新穎的基於Mamba的FGS架構。它引入了自適應掃描分數，通過結合群體層面的共性與個體層面的差異，動態指導掃描順序。在兩個公開數據集（ACDC和Synapse）以及新提出的具有挑戰性的膽道系統FGS數據集（即BTMS）上的實驗表明，ASM-UNet在CGS和FGS任務中均達到了優異的性能。我們的代碼和數據集可在https://github.com/YqunYang/ASM-UNet獲取。

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：整合群體共性與個體差異的自適應掃描Mamba網絡，用於精細分割

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

摘要

Support