  In this work, the best size for late gadolinium enhancement (LGE) magnetic
resonance imaging (MRI) images in the training dataset was determined to
optimize deep learning training outcomes. Non-extra pixel and extra pixel
interpolation algorithms were used to determine the new size of the LGE-MRI
images. A novel strategy was introduced to handle interpolation masks and
remove extra class labels in interpolated ground truth (GT) segmentation masks.
The expectation maximization, weighted intensity, a priori information (EWA)
algorithm was used for quantification of myocardial infarction (MI) in
automatically segmented LGE-MRI images. Arbitrary threshold, comparison of the
sums, and sums of differences are methods used to estimate the relationship
between semi-automatic or manual and fully automated quantification of
myocardial infarction (MI) results. The relationship between semi-automatic and
fully automated quantification of MI results was found to be closer in the case
of bigger LGE MRI images (55.5% closer to manual results) than in the case of
smaller LGE MRI images (22.2% closer to manual results).
