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Journal of Imaging Informatics in Medicine
Published in: Journal of Digital Imaging 4/2020

01-08-2020 | Original Paper

SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation

Authors: Tiejun Yang, Tingting Wu, Lei Li, Chunhua Zhu

Published in: Journal of Imaging Informatics in Medicine | Issue 4/2020

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Abstract

Since morphology of retinal blood vessels plays a key role in ophthalmological disease diagnosis, retinal vessel segmentation is an indispensable step for the screening and diagnosis of retinal diseases with fundus images. In this paper, deep convolution adversarial network combined with short connection and dense block is proposed to separate blood vessels from fundus image, named SUD-GAN. The generator adopts U-shape encode-decode structure and adds short connection block between convolution layers to prevent gradient dispersion caused by deep convolution network. The discriminator is all composed of convolution block, and dense connection structure is added to the middle part of the convolution network to strengthen the spread of features and enhance the network discrimination ability. The proposed method is evaluated on two publicly available databases, the DRIVE and STARE. The results show that the proposed method outperforms the state-of-the-art performance in sensitivity and specificity, which were 0.8340 and 0.9820, and 0.8334 and 0.9897 respectively on DRIVE and STARE, and can detect more tiny vessels and locate the edge of blood vessels more accurately.
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Literature
1.
Soomro, T. A., Afifi, A. J., Zheng, L., Soomro, S., Gao, J., Hellwich, O., Paul, M. Deep learning models for retinal blood vessels segmentation: a review [J]. IEEE Access, 2019, 7(1):71696-71717. 1–1.CrossRef
2.
Azzopardi, G., Strisciuglio, N., Vento, M., Petkov, N. Trainable COSFIRE filters for vessel delineation with application to retinal images [J]. Med Image Anal, 2015,19(1): 46–57.CrossRef
3.
Zhao, Y., Rada, L., Chen, K., Harding, S. P., Zheng, Y. Automated vessel segmentation using infinite perimeter active contour model with hybrid region information with application to retinal images [J]. IEEE Trans Med Imaging, 2015,34(9):1797–1807.CrossRef
4.
Jiang, Z., Yepez, J., An, S., Ko, S. Fast, accurate and robust retinal vessel segmentation system [J]. Biocybern Biomed Eng, 2017, 37:412–421.CrossRef
5.
Liang E H. Retinal vascular segmentation based on improved matched filtering [J]. Inf Communication, 2018, 08:6-9.
6.
J.I. Orlando, E. Prokofyeva, M.B. Blaschko, A discriminatively trained fully connected conditional random field model for blood vessel segmentation in fundus images [J], Trans Biomed Eng2017, 64 (1) , 16–27.CrossRef
7.
Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., Barman, S. A. An ensemble classification-based approach applied to retinal blood vessel segmentation [J]. IEEE Trans Biomed Eng, 2012,59(9): 2538–2548.CrossRef
8.
Shelhamer, E., Long, J., Darrell, T. Fully Convolutional Networks for Semantic Segmentation. arXiv preprint arXiv:1411.4038v3.
9.
Ronneberger, O., Fischer, P., Brox, T. (2015).U-Net: Convolutional Networks for Biomedical Image Segmentation. Med Image Comp Comp-Assisted Interv—MICCAI 2015, 234–241.
10.
Badrinarayanan, V., Kendall, A., Cipolla, R. SegNet: a Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv preprint arXiv: 1511.00561v3
11.
Mo, J., Zhang, L. Multi-level deep supervised networks for retinal vessel segmentation [J]. Int J Comput Assist Radiol Surg, 2017, 12(12), 2181–2193.CrossRef
12.
Hu, K., Zhang, Z., Niu, X., Zhang, Y., Cao, C., Xiao, F., Gao, X. (2018).Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing, 309, 179–191.CrossRef
13.
Oliveira, A., Pereira, S., Silva, C. A. Retinal vessel segmentation based on fully convolutional Neural networks. Expert Syst Appl, 2018,112, 229–243.CrossRef
14.
Guo, S., Wang, K., Kang, H., Zhang, Y., Gao, Y., Li, T. BTS-DSN: deeply supervised neural network with short connections for retinal vessel segmentation. Int J Med Inform, 2019, 126:105–113.CrossRef
15.
Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets [C], International Conference on Neural Information Processing Systems. MIT Press, 2014:2672-2680.
16.
Mirza M, Osindero S. Conditional generative adversarial nets [J]. Comput Therm Sci, 2014: 2672-2680.
17.
Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks. Comp Sci, 2015.
18.
Moeskops P, Veta M, Lafarge M W, et al.. Adversarial training and dilated convolutions for brain MRI segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support, 2017: 56-64, 2017CrossRef
19.
Rezaei M, Harmuth K, Gierke W, et al. A conditional adversarial network for semantic segmentation of brain tumor. Brain Lesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2018: 241-253, 2018
20.
Shankaranarayana SM, Ram K, Mitra K, et al. Joint optic disc and cup segmentation using fully convolutional and adversarial networks. Fetal Infant Ophthalmic Med Image Anal, 2017:168-176, 2017CrossRef
21.
Lahiri A, Ayush K, Biswas P K, et al. Generative adversarial learning for reducing manual annotation in semantic segmentation on large scale miscroscopy images: automated vessel segmentation in retinal fundus image as test case. 2017 IEEE Conf Comp Vision Pattern Recog Workshops (CVPRW) Workshops (CVPRW), 2017:794-800, 2017CrossRef
22.
He K, Zhang X, Ren S, Sun J Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
23.
Huang G, Liu Z, van der Maaten L, Weinberger KQ (2017). Densely connected convolutional networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
24.
Wu Chenyue, Yi Benshun, Zhang Yungang, Huang Song, Feng Yu. Image segmentation of retinal vessels based on improved convolutional neural network. Acta Opt Sin, 2018, 38(11):133-139.CrossRef
Metadata
Title
SUD-GAN: Deep Convolution Generative Adversarial Network Combined with Short Connection and Dense Block for Retinal Vessel Segmentation
Authors
Tiejun Yang
Tingting Wu
Lei Li
Chunhua Zhu
Publication date
01-08-2020
Publisher
Springer International Publishing
Published in
Journal of Imaging Informatics in Medicine / Issue 4/2020
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-020-00339-9

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