Effects of Image Size on Deep Learning

In this paper the main objective is to determine the best size of late gadolinium enhancement (LGE)-magnetic resonance imaging (MRI) images for the training dataset to achieve optimal deep learning training outcomes. To determine the new size of LGE-MRI images from the reference training dataset, non-extra pixel and extra pixel interpolation algorithms are used. A novel strategy based on thresholding, median filtering, and subtraction operations is introduced and applied to remove extra class labels in interpolated ground truth (GT) segmentation masks. Fully automated quantification is achieved using the expectation maximization, weighted intensity, a priori information (EWA) algorithm, and the outcome of automatic semantic segmentation of LGE-MRI images with the convolutional neural network (CNN). In the experiments, common class metrics are used to evaluate the quality of semantic segmentation with a CNN architecture of interest (U-net) against the GT segmentation. Arbitrary threshold, comparison of the sums, and sums of differences are criteria or options used to estimate the relationship between semi-automatic and fully automated quantification of MI results. A close relationship between semi-automatic or manual and fully automated quantification of MI results was more identified in the case involving the dataset of bigger LGE MRI images than in that of the dataset of smaller LGE-MRI images where the best quantification results based on the dataset of bigger LGE MRI images were 55.5% closer the manual or semiautomatic results while the best quantification results based on the dataset of smaller LGE MRI images were 22.2% closer the manual results.