Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks-Reference-Cited by-同舟云学术

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Fully Automatic Arteriovenous Segmentation in Retinal Images via Topology-Aware Generative Adversarial Networks

Published:2020-07-28 Issue:3 Volume:12 Page:323-334
ISSN:1913-2751
Container-title:Interdisciplinary Sciences: Computational Life Sciences
language:en
Short-container-title:Interdiscip Sci Comput Life Sci

Author:

Yang Jingwen,Dong Xinran,Hu Yu,Peng Qingsheng,Tao Guihua,Ou Yangming,Cai Hongmin^ORCID,Yang Xiaohong

Funder

National Natural Science Foundation of China

Science and Technology Program of Guangzhou

Key-Area Research and Development of Guangdong Province

Health & Medical Collaborative Innovation Project of Guangzhou City

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Computer Science Applications,General Biochemistry, Genetics and Molecular Biology

Link

https://link.springer.com/content/pdf/10.1007/s12539-020-00385-5.pdf

Reference37 articles.

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2. Beck A, Teboulle M (2009) Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems. IEEE Trans Image Process 18(11):2419–2434

3. Cai H, Yang Z, Cao X, Xia W, Xu X (2014) A new iterative triclass thresholding technique in image segmentation. IEEE Trans Image Process 23(3):1038–1046

4. Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848

5. Cheung CYL, Zheng Y, Hsu W, Lee ML, Lau QP, Mitchell P, Wang JJ, Klein R, Wong TY (2011) Retinal vascular tortuosity, blood pressure, and cardiovascular risk factors. Ophthalmology 118(5):812–818

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