  Eye movements, blinking and other motion during the acquisition of optical
coherence tomography (OCT) can lead to artifacts, when processed to OCT
angiography (OCTA) images. Affected scans emerge as high intensity (white) or
missing (black) regions, resulting in lost information. The aim of this
research is to fill these gaps using a deep generative model for OCT to OCTA
image translation relying on a single intact OCT scan. Therefore, a U-Net is
trained to extract the angiographic information from OCT patches. At inference,
a detection algorithm finds outlier OCTA scans based on their surroundings,
which are then replaced by the trained network. We show that generative models
can augment the missing scans. The augmented volumes could then be used for 3-D
segmentation or increase the diagnostic value.
