  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.
