Deep Learning Automated Segmentation of the Left Ventricle for Spin-Echo Cardiac Diffusion Tensor Imaging (cDTI)

Motivation: Segmentation is central to cDTI post-processing, but remains subjective, time-intensive, and observer-dependent. Faster methods are needed. Goal(s): To develop and validate a U-Net for automating and standardizing left ventricle segmentations for cDTI. Our target was for U-net generated masks to yield cDTI metric maps within 5% of ground-truth and Dice scores comparable to a human reader. Approach: We developed a U-Net to automatically segment cDTI data then compared generated masks to expert annotations. Results: Median Dice score was 0.79 with cDTI metrics within 5% of ground truth. A multiple-reader study demonstrated the need for further generalization of datasets at different resolutions. Impact: An automated U-Net approach to cardiac DTI segmentation of the left ventricle minimizes segmentation variability, reduces processing time, and preserves cDTI metric measurement accuracy.