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In this work, we implement a fully convolutional segmenter featuring both alearned group structure and a regularized weight-pruner to reduce the highcomputational cost in volumetric image segmentation. We validated our frameworkon the ACDC dataset featuring one healthy and four pathology groups imagedthroughout the cardiac cycle. Our technique achieved Dice scores of 96.8% (LVblood-pool), 93.3% (RV blood-pool) and 90.0% (LV Myocardium) with five-foldcross-validation and yielded similar clinical parameters as those estimatedfrom the ground truth segmentation data. Based on these results, this techniquehas the potential to become an efficient and competitive cardiac imagesegmentation tool that may be used for cardiac computer-aided diagnosis,planning, and guidance applications.

L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI