The annotation scarcity of medical image segmentation poses challenges in collecting sufficient training data for deep learning models. Specifically, models trained on limited data may not generalize well to other unseen data domains, resulting in a domain shift issue. Consequently, domain generalization (DG) is developed to boost the performance of segmentation models on unseen domains. However, the DG setup requires multiple source domains, which impedes the efficient deployment of segmentation algorithms in clinical scenarios. To address this challenge and improve the segmentation model's generalizability, we propose a novel approach called the Frequency-mixed Single-source Domain Generalization method (FreeSDG). By analyzing the frequency's effect on domain discrepancy, FreeSDG leverages a mixed frequency spectrum to augment the single-source domain. Additionally, self-supervision is constructed in the domain augmentation to learn robust context-aware representations for the segmentation task. Experimental results on five datasets of three modalities demonstrate the effectiveness of the proposed algorithm. FreeSDG outperforms state-of-the-art methods and significantly improves the segmentation model's generalizability. Therefore, FreeSDG provides a promising solution for enhancing the generalization of medical image segmentation models, especially when annotated data is scarce. The code is available at https://github.com/liamheng/Non-IID_Medical_Image_Segmentation.

翻译：医学图像分割的标注稀缺性给深度学习模型收集充足的训练数据带来了挑战。具体而言，在有限数据上训练的模型可能无法很好地泛化到其他未见过的数据域，导致域偏移问题。为此，域泛化（DG）技术被提出以提升分割模型在未知域上的性能。然而，DG设置需要多个源域，这阻碍了分割算法在临床场景中的高效部署。为解决这一挑战并提高分割模型的泛化能力，我们提出了一种名为频域混合单源域泛化方法（FreeSDG）的新方案。通过分析频域对域差异的影响，FreeSDG利用混合频谱对单源域进行增强。此外，我们在域增强中构建了自监督机制，以学习面向分割任务的鲁棒上下文感知表示。在三个模态的五个数据集上的实验结果表明了所提算法的有效性。FreeSDG优于当前最先进的方法，并显著提升了分割模型的泛化能力。因此，FreeSDG为增强医学图像分割模型的泛化性（尤其在标注数据稀缺的情况下）提供了一种有前景的解决方案。代码开源地址为https://github.com/liamheng/Non-IID_Medical_Image_Segmentation。