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Synthesis of annotated pathological retinal OCT data with pathology-induced deformations

In this work, a generative adversarial network (GAN)-based pipeline for the generation of realistic retinal optical coherence tomography (OCT) images with available pathological structures and ground truth anatomical and pathological annotations is established. The emphasis of the proposed image generation approach lies especially on the simulation of the pathology-induced deformations of the retinal layers around a pathological structure. Our experiments demonstrate the realistic appearance of the images as well as their applicability for the training of neural networks.