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Supervised learning-based retinal vascular segmentation by M-UNet full convolutional neural network

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Abstract

The accurate vessel segmentation for retinal image is the most conducive to early diagnosis of various eye-related diseases. Most deep learning models are used to segment the vessel by green channel grey image and random sampling of whole image that lead to insufficient training samples and low accuracy probably. To address this issue, a novel retinal vessel segmentation model based on deep learning is proposed, which is called Multichannel U-Net (M-UNet) combined with multi-scale equalization sampling for extracting vessel networks. The multi-scale equalization sampling patches are used to splice an image as new data. And the M-UNet structure is utilized to train the retinal vessel segmentation model. The input of the model is the three channel image replacing the grey image. The DRIVE and STARE database are used to evaluate the performance. Experimental results indicate that the proposed method can obtain better vessel networks, especially for the extraction of peripheral vascular structure. The average accuracy of DRIVE is 0.9716, sensitivity is 0.8168, specificity is 0.9860, and Area Under Curve (AUC) is up to 0.9843. The performances are more competitive than state-of-the-art methods.

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Acknowledgements

This work is supported by the Natural Science Foundation of Fujian Province (Grant No. 2020J01573), Educational Science and Planning Project of Fujian Province (FJJKCG20-178).

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Authors and Affiliations

  1. Fuzhou Institute of Technology, Fuzhou, 350506, China

    Heng Dong

  2. College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China

    Ting Zhang, Tianyi Zhang & Lifang Wei

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  1. Heng Dong
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  2. Ting Zhang
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Correspondence to Lifang Wei.

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Dong, H., Zhang, T., Zhang, T. et al. Supervised learning-based retinal vascular segmentation by M-UNet full convolutional neural network. SIViP 16, 1755–1761 (2022). https://doi.org/10.1007/s11760-022-02132-3

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  • DOI: https://doi.org/10.1007/s11760-022-02132-3

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