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.

本文介绍一种称为“分布宝藏”的多源域泛化方法，它采用随机样本并构造了一个前所未有的搜索空间以获得强韧性模型，为了明确学习域不变表现，进一步制定了“Style Mix”策略通过随机混合通道高效地混合特征风格。在两项医学分割任务中的广泛实验表明，TriD在看不见目标域数据上的泛化性能优于其他DG方法。

分布中的宝藏：基于领域随机化的2D医学图像分割多源领域泛化