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SAT-Net: a side attention network for retinal image segmentation

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Abstract

Retinal vessel segmentation plays an important role in the automatic assessment of eye health. Deep learning technology has been extensively employed in medical image segmentation. Specifically, U-net based methods achieve great success in medical image segmentation. However, due to its continuous pooling layer and convolution operation, the spatial information and texture information of the image are destroyed. To address this issue, we propose a SAT-Net that integrates side attention and dense atrous convolution block, which also consists of multi-scale input so that the network can retain more features of the image of the encoder stage. The dense atrous convolution block enables multiple receptive field fusion, which preserves the context information of the image, and the side attention mechanism further enhances the high-level information of the encoded features and reduces the noise in the feature map. We apply this method to different retinal image segmentation datasets and compare with the other methods. The experimental results demonstrate the effectiveness of the proposed method.

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Acknowledgements

This study was supported by the Shanghai Science and Technology Commission (No.18411952800).

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Authors and Affiliations

  1. Shanghai University of Engineering Science, 333 Longteng Road, Songjiang District, Shanghai, 201620, China

    Huilin Tong, Zhijun Fang & Yongbin Gao

  2. Shanghai Changzheng Hospital, Shanghai, 415 Fengyang Road, Huangpu District, Shanghai, 200003, China

    Ziran Wei & Qingping Cai

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  1. Huilin Tong
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  2. Zhijun Fang
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  3. Ziran Wei
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  5. Yongbin Gao
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Correspondence to Huilin Tong.

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Tong, H., Fang, Z., Wei, Z. et al. SAT-Net: a side attention network for retinal image segmentation. Appl Intell 51, 5146–5156 (2021). https://doi.org/10.1007/s10489-020-01966-z

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