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.
@article{arxiv.1907.12930,
title = {Attention Guided Network for Retinal Image Segmentation},
author = {Shihao Zhang and Huazhu Fu and Yuguang Yan and Yubing Zhang and Qingyao Wu and Ming Yang and Mingkui Tan and Yanwu Xu},
journal= {arXiv preprint arXiv:1907.12930},
year = {2019}
}
Comments
Accepted to MICCAI 2019. Project page: (https://github.com/HzFu/AGNet)