  Automated optic disc (OD) and optic cup (OC) segmentation in fundus images is
relevant to efficiently measure the vertical cup-to-disc ratio (vCDR), a
biomarker commonly used in ophthalmology to determine the degree of
glaucomatous optic neuropathy. In general this is solved using coarse-to-fine
deep learning algorithms in which a first stage approximates the OD and a
second one uses a crop of this area to predict OD/OC masks. While this approach
is widely applied in the literature, there are no studies analyzing its real
contribution to the results. In this paper we present a comprehensive analysis
of different coarse-to-fine designs for OD/OC segmentation using 5 public
databases, both from a standard segmentation perspective and for estimating the
vCDR for glaucoma assessment. Our analysis shows that these algorithms not
necessarily outperfom standard multi-class single-stage models, especially when
these are learned from sufficiently large and diverse training sets.
Furthermore, we noticed that the coarse stage achieves better OD segmentation
results than the fine one, and that providing OD supervision to the second
stage is essential to ensure accurate OC masks. Moreover, both the single-stage
and two-stage models trained on a multi-dataset setting showed results in pair
or even better than other state-of-the-art alternatives, while ranking first in
REFUGE for OD/OC segmentation. Finally, we evaluated the models for vCDR
prediction in comparison with six ophthalmologists on a subset of AIROGS
images, to understand them in the context of inter-observer variability. We
noticed that vCDR estimates recovered both from single-stage and coarse-to-fine
models can obtain good glaucoma detection results even when they are not highly
correlated with manual measurements from experts.
