A Deep-Learning Approach for Automated OCT En-Face Retinal Vessel Segmentation in Cases of Optic Disc Swelling Using Multiple En-Face Images as Input.

Journal: Translational vision science & technology
Published Date: Mar 1, 2020

Abstract

PURPOSE: In cases of optic disc swelling, segmentation of projected retinal blood vessels from optical coherence tomography (OCT) volumes is challenging due to swelling-based shadowing artifacts. Based on our hypothesis that simultaneously considering vessel information from multiple projected retinal layers can substantially increase vessel visibility, in this work, we propose a deep-learning-based approach to segment vessels involving the simultaneous use of three OCT en-face images as input.

Authors

  • Mohammad Shafkat Islam
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
  • Jui-Kai Wang
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
  • Samuel S Johnson
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA, USA.
  • Matthew J Thurtell
    Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA, USA.
  • Randy H Kardon
    Iowa City VA Health Care System and Iowa City VA Center for the Prevention and Treatment of Visual Loss, Iowa City, IA, USA.
  • Mona K Garvin
    Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, 52242, United States; Iowa City VA Health Care System, Iowa City, IA, 52246, United States. Electronic address: mona-garvin@uiowa.edu.

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