The task of single-source domain generalization (SDG) in medical image segmentation is crucial due to frequent domain shifts in clinical image datasets. To address the challenge of poor generalization across different domains, we introduce a Plug-and-Play module for data augmentation called MoreStyle. MoreStyle diversifies image styles by relaxing low-frequency constraints in Fourier space, guiding the image reconstruction network. With the help of adversarial learning, MoreStyle further expands the style range and pinpoints the most intricate style combinations within latent features. To handle significant style variations, we introduce an uncertainty-weighted loss. This loss emphasizes hard-to-classify pixels resulting only from style shifts while mitigating true hard-to-classify pixels in both MoreStyle-generated and original images. Extensive experiments on two widely used benchmarks demonstrate that the proposed MoreStyle effectively helps to achieve good domain generalization ability, and has the potential to further boost the performance of some state-of-the-art SDG methods.

翻译：单源域泛化（SDG）在医学图像分割中至关重要，因为临床图像数据集常出现域偏移。为解决跨域泛化能力不足的挑战，我们提出了一种即插即用的数据增强模块——MoreStyle。MoreStyle通过放宽傅里叶空间中的低频约束，引导图像重建网络，从而多样化图像风格。借助对抗学习，MoreStyle进一步扩展风格范围，并定位潜在特征中最复杂的风格组合。为应对显著风格变化，我们引入不确定性加权损失，该损失仅强调由风格偏移导致的难分类像素，同时减轻MoreStyle生成图像和原始图像中真正难分类像素的影响。在两个广泛使用的基准数据集上的大量实验表明，所提出的MoreStyle能有效增强域泛化能力，并有潜力进一步提升某些最先进SDG方法的性能。