Assessment of Generative Adversarial Networks Model for Synthetic Optical Coherence Tomography Images of Retinal Disorders

Abstract

Purpose To assess whether a generative adversarial network (GAN) could synthesize realistic optical coherence tomography (OCT) images that satisfactorily serve as the educational images for retinal specialists, and the training datasets for the classification of various retinal disorders using deep learning (DL). Methods The GANs architecture was adopted to synthesize high-resolution OCT images trained on a publicly available OCT dataset, including urgent referrals (37,206 OCT images from eyes with choroidal neovascularization, and 11,349 OCT images from eyes with diabetic macular edema) and nonurgent referrals (8617 OCT images from eyes with drusen, and 51,140 OCT images from normal eyes). Four hundred real and synthetic OCT images were evaluated by two retinal specialists (with over 10 years of clinical retinal experience) to assess image quality. We further trained two DL models on either real or synthetic datasets and compared the performance of urgent versus nonurgent referrals diagnosis tested on a local (1000 images from the public dataset) and clinical validation dataset (278 images from Shanghai Shibei Hospital). Results The image quality of real versus synthetic OCT images was similar as assessed by two retinal specialists. The accuracy of discrimination of real versus synthetic OCT images was 59.50% for retinal specialist 1 and 53.67% for retinal specialist 2. For the local dataset, the DL model trained on real (DL_Model_R) and synthetic OCT images (DL_Model_S) had an area under the curve (AUC) of 0.99, and 0.98, respectively. For the clinical dataset, the AUC was 0.94 for DL_Model_R and 0.90 for DL_Model_S. Conclusions The GAN synthetic OCT images can be used by clinicians for educational purposes and for developing DL algorithms. Translational Relevance The medical image synthesis based on GANs is promising in humans and machines to fulfill clinical tasks.

Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection

“…In our previous study, we reported that GANs were able to synthesize realistic high-resolution OCT images and achieve a high AUC of 0.98 for screening urgent referable retinal diseases, such as choroidal neovascularization or diabetic macular edema. 13 In the current study, our DL model that trained on synthetic OCT images achieved a similar AUC of 0.94, which was comparable with that of DL models trained in all-real OCT images, such as those reported by Xu et al 5 (4036 AS-OCT images) and Fu et al 7 (8270 AS-OCT ACA images). As different reference standards were used, our study cannot be directly compared to those of Xu et al and Fu et al It should be noted that a small validation dataset makes it challenging to interpret a small difference in model performance, and the distribution of open- and closed-angle images was not the same in the two training datasets, which could also introduce training bias.…”

“…In a previous study, we proposed a GAN approach to generate realistic OCT images to serve as training datasets for DL algorithms and education images for retinal specialists. 13 We showed that DL algorithms trained with only generated images achieved performance nearly comparable to the results obtained from training on real images. In the present work, we first demonstrate that a similar technique can also generate realistic AS-OCT images.…”

“…A similar method had been reported by us previously. 13 Briefly, we modified a Google (Menlo Park, CA) Inception V3 architecture with weights pretrained on ImageNet using the transfer learning technique. 23 , 24 DL_Model_R trained exclusively with real images ( n = 20,000, randomly selected from the GAN training dataset with all 28,643 AS-OCT ACA images), and DL_Model_S trained only with synthetic images ( n = 20,000).…”

Semi-supervised generative adversarial networks for closed-angle detection on anterior segment optical coherence tomography images: an empirical study with a small training dataset

“…First, our semi-supervised GANs architecture can only synthesize images with 128×128 pixels lower than the Casia AS-OCT images' resolution. Our previous study reported a progressively grown GANs architecture to generate realistic OCT images with higher resolutions (e.g., 256×256 or above) (41). Second, we only used two independent testing datasets with small sample sizes, making the little difference in model performance challenging to interpret.…”

Deepfakes in Ophthalmology

“…9,[19][20][21][22][23][24][25][26] Since ophthalmology has been at the forefront of the DL revolution, there are numerous potential applications of synthetic images, starting with fundus 9,19,20 and optical coherence tomography (OCT). [27][28][29] Synthetic images can be modified to adjust image features such as pigmentation, 9 image quality, 30 and even disease severity. 31 One of many potential applications is as an alternative solution to increase the size and diversity of training datasets for DL algorithms.…”