A Retrospective Comparison of Deep Learning to Manual Annotations for Optic Disc and Optic Cup Segmentation in Fundus Photographs.

Journal: Translational vision science & technology
Published Date: Jun 24, 2020

Abstract

PURPOSE: Optic disc (OD) and optic cup (OC) segmentation are fundamental for fundus image analysis. Manual annotation is time consuming, expensive, and highly subjective, whereas an automated system is invaluable to the medical community. The aim of this study is to develop a deep learning system to segment OD and OC in fundus photographs, and evaluate how the algorithm compares against manual annotations.

Authors

  • Huazhu Fu
    A*STAR, Singapore, Singapore.
  • Fei Li
    Institute for Precision Medicine, Tsinghua University, Beijing, China.
  • Yanwu Xu
    School of Future Technology, South China University of Technology, Guangzhou, Guangdong Province, China.
  • Jingan Liao
    School of Computer Science and Engineering, South China University of Technology, Guangzhou, Guangdong, China.
  • Jian Xiong
    School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China; Department of Communication Engineering, Chengdu Technological University, Chengdu 611731, China.
  • Jianbing Shen
    Inception Institute of Artificial Intelligence, Abu Dhabi, United Arab Emirates.
  • Jiang Liu
    Department of Pharmacy, The Fourth Hospital of Hebei Medical University Shijiazhuang 050000, Hebei, China.
  • Xiulan Zhang
    Zhongshan Ophthalmic Center, Sun Yat-sen University, China. Electronic address: zhangxl2@mail.sysu.edu.cn.

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