A Multi-view Crossover Attention U-Net Cascade with Fourier Domain Adaptation for Multi-domain Cardiac MRI Segmentation

Cardiac image segmentation is a crucial step in clinical practice as it allows for the assessment of cardiac morphology and the quantification of image-based biomarkers. While deep learning methods have recently achieved near human-level performance on large, single-domain cine MRI datasets, their accuracy decreases considerably in more complex multi-domain settings, limiting their clinical applicability. To this end, we propose a novel multi-view crossover cascade approach combined with both shape and appearance augmentations for effective multi-domain cardiac image segmentation. Our cascade consists of two Attention U-Net paths that share information across different views and an intermediate heart location crop to reduce variance and improve label balance. In addition to multiple shape augmentations (scaling, elastic deformations, grid distortions, etc.) and histogram matching, we introduce multi-scale Fourier Domain Adaptation to cardiac image analysis. We evaluate both the crossover cascade and the augmentations on the cine MRI dataset of the M&Ms-2 challenge and outperform a U-Net benchmark by respective Dice score increases of ∼∼0.02 and ∼∼0.03.