Domain Generalization (DG) aims to reduce domain shifts between domains to achieve promising performance on the unseen target domain, which has been widely practiced in medical image segmentation. Single-source domain generalization (SDG) is the most challenging setting that trains on only one source domain. Although existing methods have made considerable progress on SDG of medical image segmentation, the performances are still far from the applicable standards when faced with a relatively large domain shift. In this paper, we leverage the Segment Anything Model (SAM) to SDG to greatly improve the ability of generalization. Specifically, we introduce a parallel framework, the source images are sent into the SAM module and normal segmentation module respectively. To reduce the calculation resources, we apply a merging strategy before sending images to the SAM module. We extract the bounding boxes from the segmentation module and send the refined version as prompts to the SAM module. We evaluate our model on a classic DG dataset and achieve competitive results compared to other state-of-the-art DG methods. Furthermore, We conducted a series of ablation experiments to prove the effectiveness of the proposed method. The code is publicly available at https://github.com/SARIHUST/SAMMed.

翻译：域泛化旨在减少域之间的域偏移，从而在未见过的目标域上取得良好性能，这一方法已广泛应用于医学图像分割。单源域泛化是最具挑战性的场景，仅依赖单一源域进行训练。尽管现有方法在医学图像分割的单源域泛化方面取得了显著进展，但当面临较大的域偏移时，其性能仍远未达到应用标准。本文利用Segment Anything Model提升单源域泛化能力。具体而言，我们提出一个并行框架，源图像分别输入SAM模块和常规分割模块。为降低计算资源消耗，我们在将图像输入SAM模块前应用合并策略。我们从分割模块中提取边界框，并将优化后的版本作为提示输入SAM模块。我们在经典域泛化数据集上评估模型，并与其他先进域泛化方法相比取得了具有竞争力的结果。此外，我们进行了一系列消融实验以验证所提方法的有效性。代码已公开于https://github.com/SARIHUST/SAMMed。