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Domain generalization typically requires data from multiple source domainsfor model learning. However, such strong assumption may not always hold inpractice, especially in medical field where the data sharing is highlyconcerned and sometimes prohibitive due to privacy issue. This paper studiesthe important yet challenging single domain generalization problem, in which amodel is learned under the worst-case scenario with only one source domain todirectly generalize to different unseen target domains. We present a novelapproach to address this problem in medical image segmentation, which extractsand integrates the semantic shape prior information of segmentation that areinvariant across domains and can be well-captured even from single domain datato facilitate segmentation under distribution shifts. Besides, a test-timeadaptation strategy with dual-consistency regularization is further devised topromote dynamic incorporation of these shape priors under each unseen domain toimprove model generalizability. Extensive experiments on two medical imagesegmentation tasks demonstrate the consistent improvements of our method acrossvarious unseen domains, as well as its superiority over state-of-the-artapproaches in addressing domain generalization under the worst-case scenario.

Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary