  Automatic quantification of perifoveal vessel densities in optical coherence
tomography angiography (OCT-A) images face challenges such as variable intra-
and inter-image signal to noise ratios, projection artefacts from outer
vasculature layers, and motion artefacts. This study demonstrates the utility
of deep neural networks for automatic quantification of foveal avascular zone
(FAZ) parameters and perifoveal vessel density of OCT-A images in healthy and
diabetic eyes. OCT-A images of the foveal region were acquired using three
OCT-A systems: a 1060nm Swept Source (SS)-OCT prototype, RTVue XR Avanti
(Optovue Inc., Fremont, CA), and the ZEISS Angioplex (Carl Zeiss Meditec,
Dublin, CA). Automated segmentation was then performed using a deep neural
network. Four FAZ morphometric parameters (area, min/max diameter, and
eccentricity) and perifoveal vessel density were used as outcome measures. The
accuracy, sensitivity and specificity of the DNN vessel segmentations were
comparable across all three device platforms. No significant difference between
the means of the measurements from automated and manual segmentations were
found for any of the outcome measures on any system. The intraclass correlation
coefficient (ICC) was also good (> 0.51) for all measurements. Automated deep
learning vessel segmentation of OCT-A may be suitable for both commercial and
research purposes for better quantification of the retinal circulation.
