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. Source code is available at https://github.com/zhaohaoyu376/morestyle.

翻译：单源域泛化（SDG）在医学图像分割任务中至关重要，这是由于临床图像数据集中频繁出现的域偏移。为解决跨不同域泛化能力差的问题，我们引入了一个名为MoreStyle的即插即用数据增强模块。MoreStyle通过放宽傅里叶空间中的低频约束来多样化图像风格，从而指导图像重建网络。借助对抗学习的帮助，MoreStyle进一步扩展了风格范围，并精确定位了潜在特征中最复杂的风格组合。为处理显著的风格变化，我们引入了一种不确定性加权损失。该损失强调仅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实的难分类像素的影响。在两个广泛使用的基准数据集上进行的大量实验表明，所提出的MoreStyle能有效帮助实现良好的域泛化能力，并具有进一步提升一些最先进的SDG方法性能的潜力。源代码可在https://github.com/zhaohaoyu376/morestyle获取。