Abstract
Retinal vessel segmentation is the main step in the analysis of fundus images. However, gray-scales’ uneven distribution, complex structure, and serious noise interference bring difficulties of automatic retinal vessels segmentation on fundus images. To solve these problems, we present an effective retinal vessel segmentation model, SDUW-Net, in this paper. The same scale dense connection is designed to improve U-Net’s structure and remove different scales dense connections to accelerate the training speed. We use skip connections to merges the features between shallow layers and deep layers to retain more features that may be lost in the process of down sampling and convolution. Experimental results on the DRIVE dataset show that the retinal vessels can be effectively segmented out by our proposed SDUW-Net, which has AUC of 0.9811 with low computation and short training time.
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Acknowledgment
This work was supported by Education and Scientific Research Project of Middle and Young Teachers in Fujian Province (JAT180394); National Natural Science Foundation of China (61972187); Fujian Provincial Leading Project (2019H0025); Natural Science Foundation of Fujian Province (2019J01756); Research Project of Minjiang University (MYK18048); Open Fund Project of Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University) (MJUKF-IPIC201811).
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Lin, H., Kang, H., Cao, X. (2020). SDUW-Net: An Effective Retinal Vessel Segmentation Model. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12488. Springer, Cham. https://doi.org/10.1007/978-3-030-62463-7_18
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DOI: https://doi.org/10.1007/978-3-030-62463-7_18
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