A size-invariant convolutional network with dense connectivity applied to retinal vessel segmentation measured by a unique index.

Journal: Computer methods and programs in biomedicine
Published Date: May 31, 2020

Abstract

BACKGROUND AND OBJECTIVES: Retinal vessel segmentation (RVS) helps in diagnosing diseases such as hypertension, cardiovascular diseases, and others. Convolutional neural networks are widely used in RVS tasks. However, how to comprehensively evaluate the segmentation results and how to improve the networks' learning ability are two great challenges.

Authors

  • Zhongshuo Zhuo
    School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China. Electronic address: zszhuo@m.scnu.edu.cn.
  • Jianping Huang
    School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China. Electronic address: jianping@m.scnu.edu.cn.
  • Ke Lu
    University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China. Electronic address: luk@ucas.ac.cn.
  • Daru Pan
    School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China. Electronic address: pandr@scnu.edu.cn.
  • Shouting Feng
    School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China. Electronic address: fst@scnu.edu.cn.

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