ReTri: Progressive domain bridging via representation disentanglement and triple-level consistency-driven feature...

Unsupervised domain adaptation (UDA) in medical image segmentation presents significant challenges due to substantial cross-domain disparities and the inherent absence of target domain annotations. In this study, to address these challenges, we propose an end-to-end progressive domain bridging framework based on representation disentanglement and triple-level consistency-driven feature alignment, referred to as ReTri, that synergistically integrates a representation disentanglement-based image alignment (RDIA) module with a novel triple-level consistency-driven feature alignment (TCFA) module. In particular, the RDIA module aims to establish an initial domain bridge by decoupling and aligning fundamental visual disparities through disentangled representation learning, while the novel TCFA module hierarchically bridges remaining cross-domain semantic discrepancies and feature distribution disparities via two novel consistency-driven alignment mechanisms: 1) attention-guided semantics-level consistency alignment, where we purposely design a bi-attentive semantic feature extraction (BSFE) component coupled with an attention-adaptive semantic consistency (ASC) loss function, facilitating dynamic alignment of high-level semantic representations across domains, and 2) multi-view dual-level mixing consistency alignment, consisting of Feature-Cut consistent self-ensembling (FCCS) and Trans-Cut consistent self-ensembling (TCCS) components. These two components operate within intermediate mixing spaces to ensure robust knowledge transfer through complementary feature- and prediction-level consistency regularization. Extensive experimental evaluations are conducted on four challenging datasets (Lumbar Spine CT-MR, Cardiac CT-MR, Cross-domain Echocardiography, and Multi-center Prostate MR) across seven UDA-based segmentation scenarios and two external validation scenarios. Our framework achieves superior performance over the best state-of-the-art (SOTA) methods on following UDA-based segmentation scenarios: +2.9% DSC for spine CT → MR segmentation, +3.6% and +2.4% DSC for bidirectional cardiac CT↔MR segmentation, +1.7% and +2.3% DSC for bidirectional cross-center cross-vendor Echocardiography (CAMUS↔EchoNet-Dynamic) segmentation, and +12.2% and +12.0% DSC for bidirectional multi-center prostate MR segmentation. The source code and the datasets are publicly available at https://github.com/xiaorugao999/ReTri.