Abstract: Learning the semantic information and location information of feature map is essential to produce ideal results in retinal image segmentation. Recently, convolutional neural networks have shown strong capabilities in extracting valid information from feature maps. However, convolution and pooling operations filter out some useful information. This paper proposes a new skip attention guided network (SAG-Net) to save feature map's semantic and location information and guide the expansion work. In SAG-Net, the Skip Attention Gate (SAtt) module is first introduced, which is used as a sensitive extension path to pass the semantic information and location information of previous feature maps, not only helps eliminate noise, but further reduces the negative effects of the background. Secondly, the SAG-Net model is further optimized by merging image pyramids to preserve contextual features. On the Drishti-GS1 dataset, the joint disc and cup segmentation task proves the effectiveness of our proposed method. Comprehensive results show that the proposed method is superior to the original U-Net method and other recent methods for disc and cup segmentation.

Key words: convolutional neural network, image segmentation, skip attention gate, extension path