  Purpose: Optical Coherence Tomography Angiography (OCT-A) permits
visualization of the changes to the retinal circulation due to diabetic
retinopathy (DR), a microvascular complication of diabetes. We demonstrate
accurate segmentation of the vascular morphology for the superficial capillary
plexus and deep vascular complex (SCP and DVC) using a convolutional neural
network (CNN) for quantitative analysis.
  Methods: Retinal OCT-A with a 6x6mm field of view (FOV) were acquired using a
Zeiss PlexElite. Multiple-volume acquisition and averaging enhanced the vessel
network contrast used for training the CNN. We used transfer learning from a
CNN trained on 76 images from smaller FOVs of the SCP acquired using different
OCT systems. Quantitative analysis of perfusion was performed on the automated
vessel segmentations in representative patients with DR.
  Results: The automated segmentations of the OCT-A images maintained the
hierarchical branching and lobular morphologies of the SCP and DVC,
respectively. The network segmented the SCP with an accuracy of 0.8599, and a
Dice index of 0.8618. For the DVC, the accuracy was 0.7986, and the Dice index
was 0.8139. The inter-rater comparisons for the SCP had an accuracy and Dice
index of 0.8300 and 0.6700, respectively, and 0.6874 and 0.7416 for the DVC.
  Conclusions: Transfer learning reduces the amount of manually-annotated
images required, while producing high quality automatic segmentations of the
SCP and DVC. Using high quality training data preserves the characteristic
appearance of the capillary networks in each layer.
  Translational Relevance: Accurate retinal microvasculature segmentation with
the CNN results in improved perfusion analysis in diabetic retinopathy.
