GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans

TL;DR

This paper presents a GAN-based approach for super-resolution and segmentation of retinal layers in OCT scans, aiming to improve diagnostic accuracy for neurodegenerative diseases.

Contribution

It introduces a novel GAN architecture integrating U-Net and ResNet with advanced upscaling blocks and Dice loss for joint super-resolution and segmentation of OCT images.

Findings

Achieved a Dice coefficient of 0.867

Attained mIOU of 0.765

Demonstrated improved image clarity and segmentation accuracy

Abstract

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.