Retinal vascular connectivity network for deep learning OCTA construction from single OCT volume scan

Abstract

As one modality extension of optical coherence tomography (OCT), OCT angiography (OCTA) provides unrivaled capability for depth-resolved visualization of retinal vasculature at the microcapillary level resolution. For OCTA image construction, repeated OCT scans from one location are required to identify blood vessels with active blood flow. The requirement for multi-scan-volumetric OCT can reduce OCTA imaging speed, which will induce eye movements and limit the image field-of-view. In principle, the blood flow should also affect the reflectance brightness profile along the vessel direction in a single-scan-volumetric OCT. In this article, we report a retinal vascular connectivity network (RVC-Net) for deep learning OCTA construction from single-scan-volumetric OCT. We compare the effects of RVC with three adjacent B-scans and a single B-scan input models into RVC-Net. The structural-similarity index measure (SSIM) loss function was selected to optimize deep learning contrast enhancement of microstructures, i.e., microcapillaries, in OCT. This was confirmed by comparing RVC-Net performances with SSIM and mean-squared-error (MSE) loss functions. The involvement of RVC and SSIM loss function enabled microcapillary resolution OCTA construction from singlescan- volumetric OCT.