Evaluation of Extra Pixel Interpolation with Mask Processing for Medical Image Segmentation with Deep Learning

In this study, the author evaluated the use of an extra pixel interpolation algorithm with mask processing versus non-extra pixel interpolation algorithm when interpolating training dataset images and masks for medical image segmentation with deep learning. The author also examined scenarios of interpolating dataset images and masks using different algorithms: extra pixel for interpolating dataset images and non-extra pixel for interpolating dataset masks. The evaluation outcomes revealed that training on datasets consisting of images and masks both interpolated using the extra pixel bicubic interpolation (BIC) resulted in better segmentation accuracy compared to using either the non-extra pixel nearest neighbor interpolation (NN) or BIC for dataset images and NN for dataset masks. Specifically, the evaluation revealed that the BIC-BIC network was a 8.9578 % (with image size 256 x 256) and a 1.0496 % (with image size 384 x 384) increase of NN-NN network compared to the NN-BIC network which was a 8.3127 % (with image size 256 x 256) and a 0.2887 % (with image size 384 x 384) increase of NN-NN network.