Anatomy-Guided Spatiotemporal Affinity Learning for Unsupervised Domain Adaptation in Echocardiography Segmentation

Unsupervised domain adaptation for left ventricle segmentation in echocardiography is critical to enhance clinical applicability across different devices and institutions. However, it remains highly challenging due to anatomical context shift and inherent noise interference. To comprehensively tackle these challenges, we introduce an anatomy-guided spatio-temporal affinity framework from two complementary perspectives: (1) at the image level, an Anatomical Context Alignment (ACA) module uses LV-dominant cropping and four-chamber-complete (4C-complete) mirroring to adapt the source domain toward the target domain's anatomical context; (2) at the feature level, an Anatomical Affinity Refinement (AAR) module models pixel-pair anatomical affinities in spatial and temporal dimensions to enforce fine-grained anatomical consistency and suppress noise interference. Specifically, the Spatial Affinity Regularization (SAR) module encourages multi-scale feature consistency within anatomical regions, while the Temporal Affinity Refinement (TAR) module refines pseudo-label leveraging inter-frame anatomical affinities, enhancing temporal consistency without explicit motion estimation or cardiac cycle annotations. Experiments on three public datasets (CAMUS, EchoNet-Dynamic, and CardiacUDA) reveal that anatomical context shift is a key factor in domain discrepancy, and demonstrate that our method effectively alleviates this issue while outperforming previous state-of-the-art UDA methods.