Aims To develop a generative adversarial network (GAN) capable of generating realistic high-resolution anterior segment optical coherence tomography (AS-OCT) images. Methods This study included 142 628 AS-OCT B-scans from the American University of Beirut Medical Center. The Style and WAvelet based GAN architecture was trained to generate realistic AS-OCT images and was evaluated through the Fréchet Inception Distance (FID) Score and a blinded assessment by three refractive surgeons who were asked to distinguish between real and generated images. To assess the suitability of the generated images for machine learning tasks, a convolutional neural network (CNN) was trained using a dataset of real and generated images over a classification task. The generated AS-OCT images were then upsampled using an enhanced super-resolution GAN (ESRGAN) to achieve high resolution. Results The generated images exhibited visual and quantitative similarity to real AS-OCT images. Quantitative similarity assessed using FID scored an average of 6.32. Surgeons scored 51.7% in identifying real versus generated images which was not significantly better than chance (p value >0.3). The CNN accuracy improved from 78% to 100% when synthetic images were added to the dataset. The ESRGAN upsampled images were objectively more realistic and accurate compared with traditional upsampling techniques by scoring a lower Learned Perceptual Image Patch Similarity of 0.0905 compared with 0.4244 of bicubic interpolation. Conclusions This study successfully developed and leveraged GANs capable of generating high-definition synthetic AS-OCT images that are realistic and suitable for machine learning and image analysis tasks. No data are available.

中文翻译：

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使用生成对抗网络创建逼真的眼前节光学相干断层扫描图像

目标开发一种能够生成逼真的高分辨率眼前节光学相干断层扫描（AS-OCT）图像的生成对抗网络（GAN）。方法 这项研究包括来自贝鲁特美国大学医学中心的 142 628 例 AS-OCT B 扫描。基于 Style 和 WAvelet 的 GAN 架构经过训练，可生成逼真的 AS-OCT 图像，并通过 Fréchet 初始距离 (FID) 评分进行评估，并由三名屈光外科医生进行盲法评估，要求他们区分真实图像和生成图像。为了评估生成的图像对机器学习任务的适用性，使用分类任务中的真实图像和生成图像的数据集来训练卷积神经网络 (CNN)。然后使用增强型超分辨率 GAN (ESRGAN) 对生成的 AS-OCT 图像进行上采样，以实现高分辨率。结果生成的图像在视觉和定量上与真实的 AS-OCT 图像相似。使用 FID 评估的定量相似性平均得分为 6.32。外科医生在识别真实图像与生成图像方面得分为 51.7%，这并不明显优于随机图像（p 值 >0.3）。当合成图像添加到数据集中时，CNN 准确率从 78% 提高到 100%。与传统上采样技术相比，ESRGAN 上采样图像客观上更真实、更准确，其学习感知图像块相似度为 0.0905，而双三次插值为 0.4244。结论 这项研究成功开发并利用了能够生成逼真且适合机器学习和图像分析任务的高清合成 AS-OCT 图像的 GAN。无可用数据。