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DCU-net: a deformable convolutional neural network based on cascade U-net for retinal vessel segmentation

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Abstract

To further improve retinal vessel segmentation accuracy, we propose a deformable convolutional neural network based on cascade U-Net for retinal vessel segmentation: DCU-Net. The overall structure of DCU-Net is composed of two U-Net. We introduce deformable convolution to build a feature extraction module, which enhances the modeling ability of the model for vessel deformation. For improving the efficiency of information transfer between U-Net models, we use a residual channel attention module to connect U-Net. DCUNet achieves excellent results on public datasets. On DRIVE and CHASE_DB1 datasets, the Acc reaches 0.9568, 0.9664, respectively, the AUC reaches 0.9810, and 0.9872, respectively. From the experimental results, the residual channel attention module and residual deformable convolution module greatly improve the retinal vessel segmentation accuracy. The comprehensive performance of our method is better than that of some state-of-the-art methods.

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Acknowledgments

This study was funded by the National Natural Science Foundation of China (61573182), and by the Fundamental Research Funds for the Central Universities (NS2020025).

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Authors and Affiliations

  1. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Xin Yang, Zhiqiang Li, Yingqing Guo & Dake Zhou

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  1. Xin Yang
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  2. Zhiqiang Li
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  3. Yingqing Guo
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  4. Dake Zhou
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Correspondence to Xin Yang.

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Yang, X., Li, Z., Guo, Y. et al. DCU-net: a deformable convolutional neural network based on cascade U-net for retinal vessel segmentation. Multimed Tools Appl 81, 15593–15607 (2022). https://doi.org/10.1007/s11042-022-12418-w

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  • DOI: https://doi.org/10.1007/s11042-022-12418-w

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