GAN-based Super-Resolution and Segmentation of Retinal Layers in Optical coherence tomography Scans
This work addresses the need for clearer and more accurate OCT image analysis to identify biomarkers for neurodegenerative diseases like Alzheimer's, but it is incremental as it builds on existing GAN and network architectures.
The paper tackled the joint tasks of super-resolution and segmentation of retinal layers in OCT scans using a GAN-based model, achieving a Dice coefficient of 0.867 and mIOU of 0.765.
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.