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

Treasure in Distribution: A Domain Randomization based Multi-Source Domain Generalization for 2D Medical Image Segmentation