Cross-modality cardiac image segmentation model that addresses spatial-temporal confounding -- where the anatomy and imaging-modality elements of cardiac images are intertwined across space and time. DCL performs multi-dimensional causal intervention, modeling causal relationships between images and labels as well as causality along the time and space dimensions, integrating historical optimal interventions to transfer knowledge across temporal contexts. A diffusion mechanism further keeps extracted anatomical elements causally invariant across modalities. On cross-modality cardiac images (MR, CT, and ultrasound), DCL achieved a mean Dice of 0.951, outperforming other advanced segmentation methods.

3D CNN that segments seven cardiac substructures - both ventricles, both atria, the LV myocardium, ascending aorta, and pulmonary artery trunk - from cardiac CT angiography. Trained with a hybrid loss function combining multiple segmentation objectives. Developed at CUHK for the MICCAI 2017 Multi-Modality Whole Heart Segmentation (MM-WHS) challenge.