Consistency Prior Matters: Biomedical-Prompting Dual Augmentation for Domain Adaptive Medical Image Segmentation

Existing domain adaptive medical image segmentation works typically rely on style transfer techniques to mitigate the unexpected domain gap, which inevitably suffers from synthesized artifacts or unreasonable stylization. In this paper, we propose to inject biomedical-related prior knowledge (i.e., intensity and anatomical consistency) as regularization in a prompting manner, bridging the domain gap across modalities. Technically, we develop an efficient scheme called Biomedical-Prompting Dual Augmentation (BPDA) to learn domain-invariant representations by enforcing consistent model predictions across different augmented views. BPDA augments unpaired source and target images from intensity and anatomical aspects in a dual manner, while prompting the framework to fully understand the anatomical structure-invariant features. In this way, our method captures discriminative inherent representations on cross-modality scenarios. Furthermore, we also introduce a Cross-Domain Prototype Denoising (CDPD) in BPDA to refine pseudo-labeling results with the class centroids for a reliable augmentation. Extensive experiments on the cross-modality abdominal and cardiac segmentation benchmarks demonstrate the superiority of our method over state-of-the-art alternatives.