June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Validation of optical coherence tomography retinal segmentation algorithm in neuro-degenerative disease
Author Affiliations & Notes
  • Bryan Ming-Tak Wong
    School of Optometry and Vision Science, University of Waterloo, Waterloo, Ontario, Canada
  • Richard Cheng
    School of Optometry and Vision Science, University of Waterloo, Waterloo, Ontario, Canada
  • Wendy Hatch
    Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
    Kensington Eye Institute, Toronto, Ontario, Canada
  • Efrem Mandelcorn
    Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
    Kensington Eye Institute, Toronto, Ontario, Canada
  • Edward Margolin
    Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
    Kensington Eye Institute, Toronto, Ontario, Canada
  • Peng Yan
    Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
    Kensington Eye Institute, Toronto, Ontario, Canada
  • Anna Theresa Santiago
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
  • Wendy Lou
    Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
  • Christopher Hudson
    School of Optometry and Vision Science, University of Waterloo, Waterloo, Ontario, Canada
    Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada
  • Footnotes
    Commercial Relationships   Bryan Wong, None; Richard Cheng, None; Wendy Hatch, None; Efrem Mandelcorn, None; Edward Margolin, None; Peng Yan, None; Anna Santiago, None; Wendy Lou, None; Christopher Hudson, None
  • Footnotes
    Support  Ontario Brain Institute (OBI)
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 1307. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Bryan Ming-Tak Wong, Richard Cheng, Wendy Hatch, Efrem Mandelcorn, Edward Margolin, Peng Yan, Anna Theresa Santiago, Wendy Lou, Christopher Hudson; Validation of optical coherence tomography retinal segmentation algorithm in neuro-degenerative disease. Invest. Ophthalmol. Vis. Sci. 2017;58(8):1307.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : This study compared the automated retinal segmentation software of spectral domain optical coherence tomography (SD-OCT) with manually corrected segmentation in order to guide and validate its use in a prospective clinical study of neuro-degenerative diseases (NDD).

Methods : The sample comprised 30 NDD patients, including individuals with vascular cognitive impairment (n=13), frontotemporal dementia (n=6), Parkinson's disease (n=6) and Alzheimer's disease (n=5). Retinal SD-OCT scans were acquired for both eyes and then segmented using the Heidelberg Spectralis (Heidelberg, Germany) software (version 6.3.4.0). All SD-OCT scans had a quality score of 20 or better. For all the B-scans enclosed by a 3.6mm circle centered on the foveola of one randomly selected eye of each patient, one of two trained observers manually corrected erroneous internal limiting membrane, retinal nerve fiber layer (RNFL), outer plexiform layer and Bruch's membrane lines. Mean volume and mean thickness measurements for four retinal layers (total retina, RNFL, all inner retinal layers and all outer retinal layers) were then obtained. Intra-class correlation coefficients (ICCs) and Bland-Altman analyses were conducted on the data.

Results : The ICCs between the automated software and a trained observer were excellent (>0.98) for retinal thickness and volume of all 4 retinal layers. Mean differences in volume between software and observers were 0.003mm3, 0.001mm3, 0.006mm3, and -0.003mm3, respectively, for total retina, RNFL, inner retinal layers, and outer retinal layers, while mean differences in thickness were -0.004μm, 0.492μm, 1.855μm, and -1.859μm.

Conclusions : There was excellent agreement between the software and trained observers in identifying the retinal layer segmentation lines. These findings provide a foundation for future non-invasive analyses of retinal morphology in patient populations with neurodegenerative diseases.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×