Effects of Image Size on Deep Learning

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.