Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks
| Autor: | Assaf, Jad F., Mrad, Anthony Abou, Reinstein, Dan Z., Amescua, Guillermo, Zakka, Cyril, Archer, Timothy, Yammine, Jeffrey, Lamah, Elsa, Haykal, Michèle, Awwad, Shady T. |
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| Rok vydání: | 2023 |
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| Druh dokumentu: | Working Paper |
| DOI: | 10.1136/bjo-2023-324633 |
| Popis: | 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. Comment: British Journal of Ophthalmology, published online May 2, 2024 |
| Databáze: | arXiv |
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