We present a fully automated approach for quantification of left ventricle scar in Late Gadolinium Enhancement (LGE) cardiac MR (CMR), using a residual neural network. LGE images were acquired in 1075 patients with known hypertrophic cardiomyopathy in a multi-center clinical trial. Scar segmentation was performed in all patients by a CMR-trained cardiologist. For training, we use a two-phase procedure, using cropped and full-sized images consecutively. We train different models using sigmoid cross-entropy loss and Dice loss and measure average LV segmentation Dice scores of 0.77 ± 0.10 and 0.70 ± 0.12 and estimated scar percentage mismatches of 3.59% and 3.00%, respectively.