Aim. Location and extension of myocardial scar following ischemic-heart disease are strong predictors of ventricular remodeling, cardiac dysfunction and mortality. Our aim was to assess quantitatively the presence of myocardial scar from cardiac-magnetic-resonance (CMR) with late-Gadolinium-enhancement (LGE) images using a deep-learning (DL) approach. Methods. 250 CMR-LGE images of 30 patients (26 men and 4 women) with ischemic-heart disease were analyzed retrospectively. Images were acquired at Centro Cardiologico Monzino in Milan, Italy. All patients were imaged with a 1.5-T scanner (Discovery MR450, GE Healthcare, Milwaukee, WI). Scar tissue was present in all patients, but only in 215 CMR-LGE images. Scar segmentation was performed automatically with a DL approach based on ENet, a deep fully-convolutional neural network (FCNN). We investigated three different ENet configurations. The first configuration (C1) exploited ENet to retrieve directly scar segmentation from the CMR-LGE images. The second (C2) and third (C3) configurations performed scar segmentation in the myocardial region, which was previously obtained in a manual or automatic way with a state-of-the-art DL method. Each configuration was evaluated using leave-one-patient-out cross validation. Data augmentation was performed. For FCNN training and evaluation, gold-standard scar contour and myocardial area were obtained with manual segmentation performed by one expert clinician. Results. With the best-performing configuration (C2), scar-segmentation median accuracy (Acc) and Dice similarity coefficient (DSC) were 97% (inter-quartile (IQR) range = 4%) and 71% (IQR = 32%), respectively. C2 outperformed both C1 (Acc = 96%, DSC = 55%) and C3 (Acc = 95%, DSC = 51%), where Acc and DSC for myocardial segmentation were 94% and 86%, respectively.
Conclusions. DL approaches using ENet are promising in automatically segmenting scars in CMR-LGE images, achieving higher performance when limiting the search area to the manually-defined myocardial region. A complete automated approach still suffers from cumulative errors from both myocardial and scar segmentation.