  This work evaluates deep learning-based myocardial infarction (MI)
quantification using Segment cardiovascular magnetic resonance (CMR) software.
Segment CMR software incorporates the expectation-maximization, weighted
intensity, a priori information (EWA) algorithm used to generate the infarct
scar volume, infarct scar percentage, and microvascular obstruction percentage.
Here, Segment CMR software segmentation algorithm is updated with semantic
segmentation with U-net to achieve and evaluate fully automated or deep
learning-based MI quantification. The direct observation of graphs and the
number of infarcted and contoured myocardium are two options used to estimate
the relationship between deep learning-based MI quantification and medical
expert-based results.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning