Synthesizing Fundus Photographies for Training Segmentation Networks

Abstract

Automated semantic segmentation of medical imagery is a vital application using modern Deep Learning methods as they can support clinicians in their decision-making processes. However, training these models requires a large amount of training data which can be especially hard to obtain in the medical field due to ethical and data protection regulations. In this paper, we present a novel method to synthesize realistic retinal fundus images. The process mainly includes the vessel tree generation and synthesis of non-vascular regions (retinal background, fovea, and optic disc). We show that combining the (virtually) unlimited synthetic data with the limited real data during training boosts segmentation performance beyond what can be achieved with real data alone. We test the performance of the proposed method on the DRIVE and STARE databases. The results highlight that the proposed data augmentation technique achieves state-of-the-art performance and