Although recent years have witnessed the great success of convolutional neural networks (CNNs) in medical image segmentation, the domain shift issue caused by the highly variable image quality of medical images hinders the deployment of CNNs in real-world clinical applications. Domain generalization (DG) methods aim to address this issue by training a robust model on the source domain, which has a strong generalization ability. Previously, many DG methods based on feature-space domain randomization have been proposed, which, however, suffer from the limited and unordered search space of feature styles. In this paper, we propose a multi-source DG method called Treasure in Distribution (TriD), which constructs an unprecedented search space to obtain the model with strong robustness by randomly sampling from a uniform distribution. To learn the domain-invariant representations explicitly, we further devise a style-mixing strategy in our TriD, which mixes the feature styles by randomly mixing the augmented and original statistics along the channel wise and can be extended to other DG methods. Extensive experiments on two medical segmentation tasks with different modalities demonstrate that our TriD achieves superior generalization performance on unseen target-domain data. Code is available at https://github.com/Chen-Ziyang/TriD.

翻译：尽管近年来卷积神经网络（CNN）在医学图像分割领域取得了巨大成功，但由医学图像质量高度可变引起的域偏移问题阻碍了CNN在真实临床场景中的部署。域泛化（DG）方法旨在通过在源域上训练具有强泛化能力的鲁棒模型来解决这一问题。此前已有许多基于特征空间域随机化的DG方法被提出，但这些方法受限于特征风格搜索空间的有限性和无序性。本文提出一种称为"分布中的宝藏"（TriD）的多源域泛化方法，通过从均匀分布中随机采样构建前所未有的搜索空间，以获得具有强鲁棒性的模型。为显式学习域不变表示，我们在TriD中进一步设计了风格混合策略——沿通道维度随机混合增强特征与原始特征的统计量，该策略可扩展至其他DG方法。在两个不同模态的医学分割任务上的大量实验表明，我们的TriD在未见过的目标域数据上实现了优越的泛化性能。代码开源地址：https://github.com/Chen-Ziyang/TriD