Class-Aware Mutual Mixup with Triple Alignments for Semi-supervised Cross-Domain Segmentation

Semi-supervised cross-domain segmentation, also referred to as Semi-supervised domain adaptation (SSDA), aims to bridge the domain gap and enhance model performance on the target domain with the limited availability of labeled target samples, lots of unlabeled target samples, and a substantial amount of labeled source samples. However, current SSDA approaches still face challenges in attaining consistent alignment across domains and adequately addressing the segmentation performance for the tail class. In this work, we develop class-aware mutual mixup with triple alignments (CMMTA) for semi-supervised cross-domain segmentation. Specifically, we first propose a class-aware mutual mixup strategy to obtain the maximal diversification of data distribution and enable the model to focus on the tail class. Then, we incorporate our class-aware mutual mixup across three distinct pathways to establish a triple consistent alignment. We further introduce cross knowledge distillation (CKD) with two parallel mean-teacher models for intra-domain and inter-domain alignment, respectively. Experimental results on two public cardiac datasets MM-WHS and MS-CMRSeg demonstrate the superiority of our proposed approach against other state-of-the-art methods under two SSDA settings.