  Our objective is to evaluate the efficacy of methods that use deep learning
(DL) for the automatic fine-grained segmentation of optical coherence
tomography (OCT) images of the retina. OCT images from 10 patients with mild
non-proliferative diabetic retinopathy were used from a public (U. of Miami)
dataset. For each patient, five images were available: one image of the fovea
center, two images of the perifovea, and two images of the parafovea. For each
image, two expert graders each manually annotated five retinal surfaces (i.e.
boundaries between pairs of retinal layers). The first grader's annotations
were used as ground truth and the second grader's annotations to compute
inter-operator agreement. The proposed automated approach segments images using
fully convolutional networks (FCNs) together with Gaussian process (GP)-based
regression as a post-processing step to improve the quality of the estimates.
Using 10-fold cross validation, the performance of the algorithms is determined
by computing the per-pixel unsigned error (distance) between the automated
estimates and the ground truth annotations generated by the first manual
grader. We compare the proposed method against five state of the art automatic
segmentation techniques. The results show that the proposed methods compare
favorably with state of the art techniques, resulting in the smallest mean
unsigned error values and associated standard deviations, and performance is
comparable with human annotation of retinal layers from OCT when there is only
mild retinopathy. The results suggest that semantic segmentation using FCNs,
coupled with regression-based post-processing, can effectively solve the OCT
segmentation problem on par with human capabilities with mild retinopathy.
