Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information

Abstract

Regular drusen, an accumulation of material below the retinal pigment epithelium (RPE), have long been established as a hallmark early feature of nonneovascular age-related macular degeneration (AMD). Advances in imaging have expanded the phenotype of AMD to include another extracellular deposit, reticular pseudodrusen (RPD) (also termed subretinal drusenoid deposits, SDD), which are located above the RPE. We developed an approach to automatically segment retinal layers associated with regular drusen and RPD in spectral domain (SD) optical coherence tomography (OCT) images. More specifically, a shortest-path algorithm enhanced with probability maps generated through a fully convolutional neural network was used to segment drusen and RPD, as well as 11 retinal layers in SD-OCT volumes. This algorithm achieves a mean difference that is within the subpixel accuracy range drusen and RPD, alongside the other 11 retinal layers, highlighting the high robustness of this algorithm for this dataset. To the best of our knowledge, this is the first report of a validated algorithm for the automated segmentation of the retinal layers including early AMD features of RPD and regular drusen separately on SD-OCT images.