Ophthalmologists use the optic disc to cup ratio as one factor to diagnose glaucoma. Optic disc in fundus images is thearea where blood vessels and optic nerve fibers enter the retina. A cup to disc ratio (the diameter of the cup divided by thediameter of the optic disc) greater than 0.3 is considered to be suggestive of glaucoma. Therefore, we are developingautomatic methods to estimate optic disc and cup areas, and the optic disc to cup ratio. There are four steps to estimate theratio: region of interest (ROI) area detection (where optic disc is in the center) from the fundus image, optic discsegmentation from the ROI, cup segmentation from the optic disc area, and cup to optic disc ratio estimation. This paperproposes an automated method to segment the optic disc from the ROI using deep learning. A Fully Convolutional Network(FCN) with a U-Net architecture is used for the segmentation. We use fundus images from MESSIDOR dataset in thisexperiment, a public dataset containing 1,200 fundus images. We divide the dataset into five equal subsets for training andindependent testing (each set has four subsets for training and one subset for testing). The proposed method outperformsother existing algorithms. The results show 0.94 Jaccard index, 0.98 sensitivity, 0.99 specificity, and 0.99 accuracy.
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