Deep Learning for Diagnosing and Segmenting Choroidal Neovascularization in OCT Angiography in a Large Real-World Data Set.

Journal: Translational vision science & technology
PMID:

Abstract

PURPOSE: To diagnose and segment choroidal neovascularization (CNV) in a real-world multicenter clinical OCT angiography (OCTA) data set using deep learning.

Authors

  • Jie Wang
  • Tristan T Hormel
    Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
  • Kotaro Tsuboi
    Casey Eye Institute, Oregon Health & Science University, Portland, OR, USA.
  • Xiaogang Wang
    Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
  • Xiaoyan Ding
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Vision Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China.
  • Xiaoyan Peng
    Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Lab, Beijing, China.
  • David Huang
    Casey Eye Institute, Oregon Health & Science University, 3375 SW Terwilliger Blvd, Portland, OR 97205, USA.
  • Steven T Bailey
    Casey Eye Institute, Oregon Health & Science University, Portland, OR, 97239, USA.
  • Yali Jia
    Casey Eye Institute, Oregon Health and Science University, Portland, Oregon.