Automatic generation of retinal optical coherence tomography images based on generative adversarial networks.

Significance: The automatic generation algorithm of optical coherence tomography (OCT) images based on generative adversarial networks (GAN) can generate a large number of simulation images by a relatively small number of real images, which can effectively improve the classification performance.

Aim: We proposed an automatic generation algorithm for retinal OCT images based on GAN to alleviate the problem of insufficient images with high quality in deep learning, and put the diagnosis algorithm toward clinical application.

Approach: We designed a generation network based on GAN and trained the network with a data set constructed by 2014_BOE_Srinivasan and OCT2017 to acquire three models. Then, we generated a large number of images by the three models to augment age-related macular degeneration (AMD), diabetic macular edema (DME), and normal images. We evaluated the generated images by subjective visual observation, Fréchet inception distance (FID) scores, and a classification experiment.

Results: Visual observation shows that the generated images have clear and similar features compared with the real images. Also, the lesion regions containing similar features in the real image and the generated image are randomly distributed in the image field of view. When the FID scores of the three types of generated images are lowest, three local optimal models are obtained for AMD, DME, and normal images, indicating the generated images have high quality and diversity. Moreover, the classification experiment results show that the model performance trained with the mixed images is better than that of the model trained with real images, in which the accuracy, sensitivity, and specificity are improved by 5.56%, 8.89%, and 2.22%. In addition, compared with the generation method based on variational auto-encoder (VAE), the method improved the accuracy, sensitivity, and specificity by 1.97%, 2.97%, and 0.99%, for the same test set.

Conclusions: The results show that our method can augment the three kinds of OCT images, not only effectively alleviating the problem of insufficient images with high quality but also improving the diagnosis performance.