  Learning structural information is critical for producing an ideal result in
retinal image segmentation. Recently, convolutional neural networks have shown
a powerful ability to extract effective representations. However, convolutional
and pooling operations filter out some useful structural information. In this
paper, we propose an Attention Guided Network (AG-Net) to preserve the
structural information and guide the expanding operation. In our AG-Net, the
guided filter is exploited as a structure sensitive expanding path to transfer
structural information from previous feature maps, and an attention block is
introduced to exclude the noise and reduce the negative influence of background
further. The extensive experiments on two retinal image segmentation tasks
(i.e., blood vessel segmentation, optic disc and cup segmentation) demonstrate
the effectiveness of our proposed method.
