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In this paper, an optic disc and cup segmentation method is proposed usingU-Net followed by a multi-scale feature matching network. The proposed methodtargets task 2 of the REFUGE challenge 2018. In order to solve the segmentationproblem of task 2, we firstly crop the input image using single shot multiboxdetector (SSD). The cropped image is then passed to an encoder-decoder networkwith skip connections also known as generator. Afterwards, both the groundtruth and generated images are fed to a convolution neural network (CNN) toextract their multi-level features. A dice loss function is then used to matchthe features of the two images by minimizing the error at each layer. Theaggregation of error from each layer is back-propagated through the generatornetwork to enforce it to generate a segmented image closer to the ground truth.The CNN network improves the performance of the generator network withoutincreasing the complexity of the model.

REFUGE CHALLENGE 2018-Task 2:Deep Optic Disc and Cup Segmentation in Fundus Images Using U-Net and Multi-scale Feature Matching Networks