  In this paper, we design a Generative Adversarial Network (GAN)-based
solution for super-resolution and segmentation of optical coherence tomography
(OCT) scans of the retinal layers. OCT has been identified as a non-invasive
and inexpensive modality of imaging to discover potential biomarkers for the
diagnosis and progress determination of neurodegenerative diseases, such as
Alzheimer's Disease (AD). Current hypotheses presume the thickness of the
retinal layers, which are analyzable within OCT scans, can be effective
biomarkers. As a logical first step, this work concentrates on the challenging
task of retinal layer segmentation and also super-resolution for higher clarity
and accuracy. We propose a GAN-based segmentation model and evaluate
incorporating popular networks, namely, U-Net and ResNet, in the GAN
architecture with additional blocks of transposed convolution and sub-pixel
convolution for the task of upscaling OCT images from low to high resolution by
a factor of four. We also incorporate the Dice loss as an additional
reconstruction loss term to improve the performance of this joint optimization
task. Our best model configuration empirically achieved the Dice coefficient of
0.867 and mIOU of 0.765.
