AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline.

Journal: Translational vision science & technology
Published Date: Jul 8, 2022

Abstract

PURPOSE: To externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available, facilitating widespread research in ophthalmic and systemic diseases.

Authors

  • Yukun Zhou
    Centre for Medical Image Computing, University College London, London, UK.
  • Siegfried K Wagner
    National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
  • Mark A Chia
    NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
  • An Zhao
    Centre for Medical Image Computing, University College London, London, UK.
  • Peter Woodward-Court
    NIHR Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.
  • Moucheng Xu
    Centre for Medical Image Computing, University College London, London, UK.
  • Robbert Struyven
    NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, UCL Institute of Ophthalmology, London, UK.
  • Daniel C Alexander
    Centre for Medical Image Computing and Dept of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
  • Pearse A Keane
    National Institute for Health Research Biomedical Research Centre for Ophthalmology, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.

Keywords

External Resources

Popular Topics
Recent Journals