  Accurate segmentation of the optic disc from a retinal image is vital to
extracting retinal features that may be highly correlated with retinal
conditions such as glaucoma. In this paper, we propose a deep-learning based
approach capable of segmenting the optic disc given a high-precision retinal
fundus image. Our approach utilizes a UNET-based model with a VGG16 encoder
trained on the ImageNet dataset. This study can be distinguished from other
studies in the customization made for the VGG16 model, the diversity of the
datasets adopted, the duration of disc segmentation, the loss function
utilized, and the number of parameters required to train our model. Our
approach was tested on seven publicly available datasets augmented by a dataset
from a private clinic that was annotated by two Doctors of Optometry through a
web portal built for this purpose. We achieved an accuracy of 99.78\% and a
Dice coefficient of 94.73\% for a disc segmentation from a retinal image in
0.03 seconds. The results obtained from comprehensive experiments demonstrate
the robustness of our approach to disc segmentation of retinal images obtained
from different sources.
