  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.
