Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary
This addresses domain generalization in medical imaging where data sharing is limited due to privacy, enabling models to generalize from a single source domain to unseen domains.
The paper tackles single-domain generalization for medical image segmentation by learning shape priors from one source domain and using test-time adaptation to handle unseen target domains, achieving consistent improvements and outperforming state-of-the-art methods in experiments on two tasks.
Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medical field where the data sharing is highly concerned and sometimes prohibitive due to privacy issue. This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains and can be well-captured even from single domain data to facilitate segmentation under distribution shifts. Besides, a test-time adaptation strategy with dual-consistency regularization is further devised to promote dynamic incorporation of these shape priors under each unseen domain to improve model generalizability. Extensive experiments on two medical image segmentation tasks demonstrate the consistent improvements of our method across various unseen domains, as well as its superiority over state-of-the-art approaches in addressing domain generalization under the worst-case scenario.