Creating Realistic Anterior Segment Optical Coherence Tomography Images
  using Generative Adversarial Networks
Jad F. Assaf Anthony Abou Mrad Dan Z. Reinstein Guillermo Amescua Cyril Zakka Timothy Archer Jeffrey Yammine Elsa Lamah Michèle Haykal Shady T. Awwad
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.