Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks
This work addresses data scarcity in medical imaging for machine learning applications, though it is incremental as it applies existing GAN methods to a new domain.
The paper tackled the problem of generating realistic Anterior Segment Optical Coherence Tomography images using Generative Adversarial Networks, achieving results where experienced surgeons could not distinguish real from synthetic images better than chance, and a classifier accuracy improved from 78% to 100% when including synthetic images in training.
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.