Improving sensitivity and connectivity of retinal vessel segmentation via error discrimination network.

Journal: Medical physics
Published Date: May 20, 2022

Abstract

PURPOSE: Automated retinal vessel segmentation is crucial to the early diagnosis and treatment of ophthalmological diseases. Many deep-learning-based methods have shown exceptional success in this task. However, current approaches are still inadequate in challenging vessels (e.g., thin vessels) and rarely focus on the connectivity of vessel segmentation.

Authors

  • GuoYe Lin
    School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China.
  • Hanhua Bai
    Southern Medical University, 1838 shatai Road, Baiyun District, Guangzhou, 510515, Guangdong province, China.
  • Jie Zhao
    Department of Liver & Gallbladder Surgery, The First People's Hospital, Shangqiu, Henan, China.
  • Zhaoqiang Yun
    Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
  • Yangfan Chen
    Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.
  • Shumao Pang
  • Qianjin Feng
    Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China. Electronic address: qianjinfeng08@gmail.com.

Keywords

External Resources

Popular Topics
Recent Journals