  In this paper, an optic disc and cup segmentation method is proposed using
U-Net followed by a multi-scale feature matching network. The proposed method
targets task 2 of the REFUGE challenge 2018. In order to solve the segmentation
problem of task 2, we firstly crop the input image using single shot multibox
detector (SSD). The cropped image is then passed to an encoder-decoder network
with skip connections also known as generator. Afterwards, both the ground
truth and generated images are fed to a convolution neural network (CNN) to
extract their multi-level features. A dice loss function is then used to match
the features of the two images by minimizing the error at each layer. The
aggregation of error from each layer is back-propagated through the generator
network to enforce it to generate a segmented image closer to the ground truth.
The CNN network improves the performance of the generator network without
increasing the complexity of the model.
