Deep generative models for synthetic retinal image generation

Abstract

The retina is an important part of the eye, which can be used to detect eye-related diseases in advance by applying retinal imaging techniques. However, the main problem of ongoing research in this field is the shortage of synthetic retinal data to be used for further development and validation of retinal data analysis methods. To solve this problem, this thesis studies state-of-the-art deep generative models to generate synthetic retinal data from a noise without conditioning any information regarding to the retina. Synthetic retinal images are generated by Generative Adversarial Networks and Variational Autoencoders. To quantify the quality of generated retinal data, a similarity based quality assessment method is proposed. The utilization of deep generative models reveals that the global structure of the retina can be generated successfully excluding the vessel tree structure.