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Cardiac left ventricular (LV) segmentation from short-axis MRI acquired 10minutes after the injection of a contrast agent (LGE-MRI) is a necessary stepin the processing allowing the identification and diagnosis of cardiac diseasessuch as myocardial infarction. However, this segmentation is challenging due tohigh variability across subjects and the potential lack of contrast betweenstructures. Then, the main objective of this work is to develop an accurateautomatic segmentation method based on deep learning models for the myocardialborders on LGE-MRI. To this end, 2.5 D residual neural network integrated witha squeeze and excitation blocks in encoder side with specialized convolutionalhas been proposed. Late fusion has been used to merge the output of the besttrained proposed models from a different set of hyperparameters. A total numberof 320 exams (with a mean number of 6 slices per exam) were used for trainingand 28 exams used for testing. The performance analysis of the proposedensemble model in the basal and middle slices was similar as compared tointra-observer study and slightly lower at apical slices. The overall Dicescore was 82.01% by our proposed method as compared to Dice score of 83.22%obtained from the intra observer study. The proposed model could be used forthe automatic segmentation of myocardial border that is a very important stepfor accurate quantification of no-reflow, myocardial infarction, myocarditis,and hypertrophic cardiomyopathy, among others.

Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model