Early detection of Glaucoma using ZASTI
Glaucoma is a highly threatening and widespread ocular disease which may lead to permanent loss in vision.
One of the important parameters used for Glaucoma screening is the cup-to-disc ratio (CDR), which requires accurate segmentation of optic cup and disc. ZASTI proposes a novel improved architecture building upon fully convolutional networks (FCNs) by using the concept of residual learning. This new architecture for image segmentation along with extensive experimental evaluations has outperformed several state-of the art techniques for the task of joint optic disc and cup segmentation.