Segregation of neuronal-vascular components in a retinal nerve fiber layer for thickness measurement using OCT and OCT angiography.

Assessment of the circumpapillary retinal nerve fiber layer (RNFL) provides crucial knowledge on the status of the optic nerve. Current circumpapillary RNFL measurements consider only thickness, but an accurate evaluation should also consider blood vessel contribution. Previous studies considered the presence of major vessels in RNFL thickness measurements from optical coherence tomography (OCT). However, such quantitative measurements do not account for smaller vessels, which could also affect circumpapillary RNFL measurements. We present an approach to automatically segregate the neuronal and vascular components in circumpapillary RNFL by combining vascular information from OCT angiography (OCTA) and structural data from OCT. Automated segmentation of the circumpapillary RNFL using a state-of-the-art deep learning network is first performed and followed by the lateral and depth-resolved localization of the vascular component by vertically projecting the vessels along the circular scan from OCTA vessels map onto the segmented RNFL. Using this proposed approach, we compare the correlations of circumpapillary RNFL thicknesses with age at different levels of vessel exclusion (exclusion of major vessels only vs both major- and micro-vessels) and also evaluate the thickness variability in 75 healthy eyes. Our results show that the ratio of major- and micro-vessels to circumpapillary RNFL achieved a stronger correlation with aging (r = 0.478, P < .001) than the ratio with only major vessels to circumpapillary RNFL (r = 0.027, P = .820). Exclusion of blood vessels from circumpapillary RNFL thickness using OCTA imaging provides a better measure of the neuronal components and could potentially improve the diagnostic performance for disease detection.