Automated detection of preserved photoreceptor on optical coherence tomography in choroideremia based on machine learning

Zhuo Wang, Acner Camino, Ahmed M. Hagag, Jie Wang, Richard Weleber, Paul Yang, Mark Pennesi, David Huang, Dengwang Li, Jia Yali

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Optical coherence tomography (OCT) can demonstrate early deterioration of the photoreceptor integrity caused by inherited retinal degeneration diseases (IRDs). A machine learning method based on random forests was developed to automatically detect continuous areas of preserved ellipsoid zone structure (an easily recognizable part of the photoreceptors on OCT) in 16 eyes of patients with choroideremia (a type of IRD). Pseudopodial extensions protruding from the preserved ellipsoid zone areas are detected separately by a local active contour routine. The algorithm is implemented on en face images with minimum segmentation requirements, only needing delineation of the Bruch's membrane, thus evading the inaccuracies and technical challenges associated with automatic segmentation of the ellipsoid zone in eyes with severe retinal degeneration.

Original languageEnglish (US)
JournalJournal of Biophotonics
DOIs
Publication statusAccepted/In press - Jan 1 2018

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Keywords

  • Choroideremia
  • Ellipsoid zone
  • Image reconstruction
  • Machine learning
  • Medical and biomedical imaging
  • Ophthalmology
  • Optical coherence tomography
  • Photoreceptor

ASJC Scopus subject areas

  • Chemistry(all)
  • Materials Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Engineering(all)
  • Physics and Astronomy(all)

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