标题： 针对医学图像分割中单源领域泛化的完全自动化SAM 显示英文标题

标题： Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation

摘要： 尽管基于SAM的单源领域泛化医学图像分割模型可以在跨领域场景中减轻领域偏移对模型的影响，但这些模型仍然面临两个主要挑战。 首先，SAM的分割高度依赖于领域特定的专家标注提示，这使得SAM无法实现完全自动化的医学图像分割，从而限制了其在临床环境中的应用。 其次，向SAM提示编码器提供不良提示（如太小或太大的边界框）可能会导致SAM生成错误的掩码结果。 因此，我们提出了FA-SAM，这是一种用于医学图像分割的单源领域泛化框架，实现了完全自动化的SAM。 FA-SAM引入了两项关键创新：一个配备了浅层特征不确定性建模（SUFM）模块的自动提示生成模型（AGM）分支，以及一个集成到SAM掩码解码器中的图像提示嵌入融合（IPEF）模块。 具体而言，AGM通过SUFM模块对浅层特征的不确定性分布进行建模，以生成目标领域的边界框提示，从而实现与SAM的完全自动化分割。 IPEF模块将来自SAM图像嵌入和提示嵌入的多尺度信息进行融合，以捕捉目标对象的全局和局部细节，使SAM能够减轻不良提示的影响。 在公开的前列腺和视网膜血管数据集上的大量实验验证了FA-SAM的有效性，并突显了其解决上述挑战的潜力。 显示英文摘要

摘要： Although SAM-based single-source domain generalization models for medical image segmentation can mitigate the impact of domain shift on the model in cross-domain scenarios, these models still face two major challenges. First, the segmentation of SAM is highly dependent on domain-specific expert-annotated prompts, which prevents SAM from achieving fully automated medical image segmentation and therefore limits its application in clinical settings. Second, providing poor prompts (such as bounding boxes that are too small or too large) to the SAM prompt encoder can mislead SAM into generating incorrect mask results. Therefore, we propose the FA-SAM, a single-source domain generalization framework for medical image segmentation that achieves fully automated SAM. FA-SAM introduces two key innovations: an Auto-prompted Generation Model (AGM) branch equipped with a Shallow Feature Uncertainty Modeling (SUFM) module, and an Image-Prompt Embedding Fusion (IPEF) module integrated into the SAM mask decoder. Specifically, AGM models the uncertainty distribution of shallow features through the SUFM module to generate bounding box prompts for the target domain, enabling fully automated segmentation with SAM. The IPEF module integrates multiscale information from SAM image embeddings and prompt embeddings to capture global and local details of the target object, enabling SAM to mitigate the impact of poor prompts. Extensive experiments on publicly available prostate and fundus vessel datasets validate the effectiveness of FA-SAM and highlight its potential to address the above challenges.