  Purpose: To remove retinal shadows from optical coherence tomography (OCT)
images of the optic nerve head(ONH).
  Methods:2328 OCT images acquired through the center of the ONH using a
Spectralis OCT machine for both eyes of 13 subjects were used to train a
generative adversarial network (GAN) using a custom loss function. Image
quality was assessed qualitatively (for artifacts) and quantitatively using the
intralayer contrast: a measure of shadow visibility ranging from 0
(shadow-free) to 1 (strong shadow) and compared to compensated images. This was
computed in the Retinal Nerve Fiber Layer (RNFL), the Inner Plexiform Layer
(IPL), the Photoreceptor layer (PR) and the Retinal Pigment Epithelium (RPE)
layers.
  Results: Output images had improved intralayer contrast in all ONH tissue
layers. On average the intralayer contrast decreased by 33.7$\pm$6.81%,
28.8$\pm$10.4%, 35.9$\pm$13.0%, and43.0$\pm$19.5%for the RNFL, IPL, PR, and RPE
layers respectively, indicating successful shadow removal across all depths.
This compared to 70.3$\pm$22.7%, 33.9$\pm$11.5%, 47.0$\pm$11.2%,
26.7$\pm$19.0%for compensation. Output images were also free from artifacts
commonly observed with compensation.
  Conclusions: DeshadowGAN significantly corrected blood vessel shadows in OCT
images of the ONH. Our algorithm may be considered as a pre-processing step to
improve the performance of a wide range of algorithms including those currently
being used for OCT image segmentation, denoising, and classification.
  Translational Relevance: DeshadowGAN could be integrated to existing OCT
devices to improve the diagnosis and prognosis of ocular pathologies.
