  The optic disc is a crucial diagnostic feature in the eye since changes to
its physiognomy is correlated with the severity of various ocular and
cardiovascular diseases. While identifying the bulk of the optic disc in a
color fundus image is straightforward, accurately segmenting its boundary at
the pixel level is very challenging. In this work, we propose disc-centered
patch augmentation (DCPA) -- a simple, yet novel training scheme for deep
neural networks -- to address this problem. DCPA achieves state-of-the-art
results on full-size images even when using small neural networks, specifically
a U-Net with only 7 million parameters as opposed to the original 31 million.
In DCPA, we restrict the training data to patches that fully contain the optic
nerve. In addition, we also train the network using dynamic cost functions to
increase its robustness. We tested DCPA-trained networks on five retinal
datasets: DRISTI, DRIONS-DB, DRIVE, AV-WIDE, and CHASE-DB. The first two had
available optic disc ground truth, and we manually estimated the ground truth
for the latter three. Our approach achieved state-of-the-art F1 and IOU results
on four datasets (95 % F1, 91 % IOU on DRISTI; 92 % F1, 84 % IOU on DRIVE; 83 %
F1, 71 % IOU on AV-WIDE; 83 % F1, 71 % IOU on CHASEDB) and competitive results
on the fifth (95 % F1, 91 % IOU on DRIONS-DB), confirming its generality. Our
open-source code and ground-truth annotations are available at:
https://github.com/saeidmotevali/fundusdisk
