Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks

TL;DR

This study develops a GAN-based method to generate highly realistic and high-resolution anterior segment OCT images, which are useful for training machine learning models and improving diagnostic tools.

Contribution

The paper introduces SWAGAN, a novel GAN architecture that produces realistic high-resolution AS-OCT images and demonstrates their effectiveness in machine learning applications.

Findings

Surgeons could not reliably distinguish real from synthetic images.

CNN accuracy reached 100% when trained with both real and generated images.

Super-resolution techniques improved image quality beyond traditional methods.

Abstract

This paper presents the development and validation of a Generative Adversarial Network (GAN) purposed to create high-resolution, realistic Anterior Segment Optical Coherence Tomography (AS-OCT) images. We trained the Style and WAvelet based GAN (SWAGAN) on 142,628 AS-OCT B-scans. Three experienced refractive surgeons performed a blinded assessment to evaluate the realism of the generated images; their results were not significantly better than chance in distinguishing between real and synthetic images, thus demonstrating a high degree of image realism. To gauge their suitability for machine learning tasks, a convolutional neural network (CNN) classifier was trained with a dataset containing both real and GAN-generated images. The CNN demonstrated an accuracy rate of 78% trained on real images alone, but this accuracy rose to 100% when training included the generated images. This underscores the utility of synthetic images for machine learning applications. We further improved the resolution of the generated images by up-sampling them twice (2x) using an Enhanced Super Resolution GAN (ESRGAN), which outperformed traditional up-sampling techniques. In conclusion, GANs can effectively generate high-definition, realistic AS-OCT images, proving highly beneficial for machine learning and image analysis tasks.