abstract. objective
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.
methods
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 interoperator 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.
results
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.
conclusions
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.