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

• URL: http://arxiv.org/abs/2306.14058v1
• Date: Sat, 24 Jun 2023 20:48:00 GMT
• Title: Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks
• Authors: Jad F. Assaf, Anthony Abou Mrad, Dan Z. Reinstein, Guillermo Amescua, Cyril Zakka, Timothy Archer, Jeffrey Yammine, Elsa Lamah, Mich\`ele Haykal, and Shady T. Awwad
• Abstract summary: 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 on 142,628 AS- OCT B-scans.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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