SATTA: Semantic-Aware Test-Time Adaptation for Cross-Domain Medical Image Segmentation

Abstract

Cross-domain distribution shift is a common problem for medical image analysis because medical images from different devices usually own varied domain distributions. Test-time adaptation (TTA) is a promising solution by efficiently adapting source-domain distributions to target-domain distributions at test time with unsupervised manners, which has increasingly attracted important attention. Previous TTA methods applied to medical image segmentation tasks usually carry out a global domain adaptation for all semantic categories, but global domain adaptation would be sub-optimal as the influence of domain shift on different semantic categories may be different. To obtain improved domain adaptation results for different semantic categories, we propose Semantic-Aware Test-Time Adaptation (SATTA), which can individually update the model parameters to adapt to target-domain distributions for each semantic category. Specifically, SATTA deploys an uncertainty estimation module to measure the discrepancies of semantic categories in domain shift effectively. Then, a semantic adaptive learning rate is developed based on the estimated discrepancies to achieve a personalized degree of adaptation for each semantic category. Lastly, semantic proxy contrastive learning is proposed to individually adjust the model parameters with the semantic adaptive learning rate. Our SATTA is extensively validated on retinal fluid segmentation based on SD-OCT images. The experimental results demonstrate that SATTA consistently improves domain adaptation performance on semantic categories over other state-of-the-art TTA methods.