Variance Extrapolated Class-Imbalance-Aware Domain Adaptive Myocardial Segmentation in Multi-Sequence Cardiac MRI

Fully automated myocardial segmentation from cardiac magnetic resonance imaging (MRI) is vital for efficient diagnosis and treatment planning. Although numerous automated methods have been proposed, they typically focus on single MRI sequences and therefore have difficulties in generalizing across vendors and across cardiac MRI protocols. Simultaneous analysis of complementary cardiac MRI sequences, such as cine, T1 mapping, and late gadolinium enhancement (LGE) MRI, remains challenging due to their distinct image characteristics and scanner-specific variations. To address these issues, we propose an unsupervised domain adaptation approach that allows robust myocardial segmentation across multi-vendor cine, T1, and LGE MRI data. In particular, we introduce a class-imbalance self-training framework to transfer information learned from a source domain with labels to any unlabeled target domain, while maintaining consistent performance across different MRI sequences. Our framework iteratively refines segmentation accuracy by generating pseudo-labels for target data using a hardness-aware strategy, thus effectively addressing the problem of class imbalance in cardiac MRI segmentation. To mitigate data scarcity following pseudo-label selection, we employ a variance-guided vicinal feature extrapolation, which expands data points in the feature space into a probabilistic distribution. This, in turn, facilitates joint source-target training by generating a larger intersection in the feature space. Experimental results demonstrate that our framework outperforms existing methods when assessed using the Dice coefficient and Hausdorff distance. Our framework enables cardiac evaluation across MRI protocols without sequence-specific manual annotations.