Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images

Gu Zheng; Yanfeng Jiang; Ce Shi; Hanpei Miao; Xiangle Yu; Yiyi Wang; Sisi Chen; Zhiyang Lin; Weicheng Wang; Fan Lu; Meixiao Shen

doi:10.1142/s1793545821400022

Journal of Innovative Optical Health Sciences, Volume. 14, Issue 1, 2140002(2021)

Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images

Gu Zheng^1,2,3, Yanfeng Jiang^1,2,3, Ce Shi^1,2,3, Hanpei Miao^1,2,3, Xiangle Yu^1,2,3, Yiyi Wang^1,2,3, Sisi Chen^1,2,3, Zhiyang Lin^1,2,3, Weicheng Wang^1,2,3, Fan Lu^1,2,3、*, and Meixiao Shen^1,2,3

Author Affiliations

¹School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou, Zhejiang, P. R. China

²Eye Hospital and School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou, Zhejiang, P. R. China

³National Clinical Research Center for Ocular Disease Wenzhou, Zhejiang, P. R. China

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Gu Zheng, Yanfeng Jiang, Ce Shi, Hanpei Miao, Xiangle Yu, Yiyi Wang, Sisi Chen, Zhiyang Lin, Weicheng Wang, Fan Lu, Meixiao Shen. Deep learning algorithms to segment and quantify the choroidal thickness and vasculature in swept-source optical coherence tomography images[J]. Journal of Innovative Optical Health Sciences, 2021, 14(1): 2140002

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Paper Information

Received: Sep. 30, 2020

Accepted: Nov. 23, 2020

Published Online: Apr. 7, 2021

The Author Email: Lu Fan (lufan62@mail.eye.ac.cn)

DOI:10.1142/s1793545821400022

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