Cardiac cine MRI left ventricle segmentation combining deep learning and graphical models

Abstract

We developed an approach for cardiac cine magnetic resonance image (MRI) left ventricle segmentation using small training dataset with noisy manual annotations. Our approach combined the strengths of deep neural networks and normalized cut with continuous regularization. We entered U-net coarse segmentation into the regularized normalized cut module that evaluates the partitioning quality within and between segmentation regions. The resulting challenging optimization problem was efficiently solved through upper bound relaxation in an iterative manner with guaranteed convergence. Within each iteration, we derived an upper bound of the high-order normalized cut term, which was combined with image-grid continuous regularization and solved using a continuous min-cut/max- ow framework. Using 5 and 10 subjects with noisy manual labels for network training, we observed much improved segmentation accuracy and minimized effects due to the size of training dataset and the quality of training annotations.