  The question is: what size of the region of interest is likely to lead to
better training outcomes? To answer this: The U-net is used for semantic
segmentation. Image interpolation algorithms are used to double the cropped
image size and create new datasets. Depending on the selected image
interpolation algorithm category, non-original classes are created in the
ground truth images thus a filtering strategy is introduced to remove such
spurious classes. Evaluation results of effects on the myocardium segmentation
and quantification of the myocardial infarction are provided and discussed.
