  Cardiac left ventricular (LV) segmentation from short-axis MRI acquired 10
minutes after the injection of a contrast agent (LGE-MRI) is a necessary step
in the processing allowing the identification and diagnosis of cardiac diseases
such as myocardial infarction. However, this segmentation is challenging due to
high variability across subjects and the potential lack of contrast between
structures. Then, the main objective of this work is to develop an accurate
automatic segmentation method based on deep learning models for the myocardial
borders on LGE-MRI. To this end, 2.5 D residual neural network integrated with
a squeeze and excitation blocks in encoder side with specialized convolutional
has been proposed. Late fusion has been used to merge the output of the best
trained proposed models from a different set of hyperparameters. A total number
of 320 exams (with a mean number of 6 slices per exam) were used for training
and 28 exams used for testing. The performance analysis of the proposed
ensemble model in the basal and middle slices was similar as compared to
intra-observer study and slightly lower at apical slices. The overall Dice
score 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 for
the automatic segmentation of myocardial border that is a very important step
for accurate quantification of no-reflow, myocardial infarction, myocarditis,
and hypertrophic cardiomyopathy, among others.
