Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images.

Journal: Translational vision science & technology
Published Date: Jul 1, 2021

Abstract

PURPOSE: This study describes the development of a deep learning algorithm based on the U-Net architecture for automated segmentation of geographic atrophy (GA) lesions in fundus autofluorescence (FAF) images.

Authors

  • Janan Arslan
    Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
  • Gihan Samarasinghe
    School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, Australia.
  • Arcot Sowmya
    School of Computer Science and Engineering, UNSW Sydney, Sydney, Australia.
  • Kurt K Benke
    School of Engineering, University of Melbourne, Parkville, Victoria, Australia.
  • Lauren A B Hodgson
    Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye & Ear Hospital, East Melbourne, Victoria, Australia.
  • Robyn H Guymer
    Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia.
  • Paul N Baird
    Department of Surgery, Ophthalmology, University of Melbourne, Parkville, Victoria, Australia.

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