Abstract
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.
This work was done when S. Zhang is intern at CVTE Research.
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Acknowledments
This work was supported by National Natural Science Foundation of China (NSFC) 61602185 and 61876208, Guangdong Introducing Innovative and Enterpreneurial Teams 2017ZT07X183, and Guangdong Provincial Scientific and Technological Fund 2018B010107001, 2017B090901008 and 2018B010108002, and Pearl River S&T Nova Program of Guangzhou 201806010081, and CCF-Tencent Open Research Fund RAGR20190103.
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Zhang, S. et al. (2019). Attention Guided Network for Retinal Image Segmentation. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11764. Springer, Cham. https://doi.org/10.1007/978-3-030-32239-7_88
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DOI: https://doi.org/10.1007/978-3-030-32239-7_88
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