September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
A novel texture-based OCT enface image to detect and monitor glaucoma
Author Affiliations & Notes
  • Akram Belghith
    Department of Ophthalmology, University of California, San Diego, Hamilton Glaucoma Center, Shiley Eye Institute, San Diego, California, United States
  • Felipe A Medeiros
    Department of Ophthalmology, University of California, San Diego, Hamilton Glaucoma Center, Shiley Eye Institute, San Diego, California, United States
  • Christopher Bowd
    Department of Ophthalmology, University of California, San Diego, Hamilton Glaucoma Center, Shiley Eye Institute, San Diego, California, United States
  • Robert N Weinreb
    Department of Ophthalmology, University of California, San Diego, Hamilton Glaucoma Center, Shiley Eye Institute, San Diego, California, United States
  • Tu Zhuowen
    Department of Cognitive Science Division of Social Sciences, UCSD, San Diego, California, United States
  • Linda M Zangwill
    Department of Ophthalmology, University of California, San Diego, Hamilton Glaucoma Center, Shiley Eye Institute, San Diego, California, United States
  • Footnotes
    Commercial Relationships   Akram Belghith, None; Felipe Medeiros, Alcon (C), Allergan (F), Allergan (C), Ametek (F), Ametek (C), Bausch+Lomb (F), Carl-Zeiss Meditec (F), Carl-Zeiss Meditec (C), Carl Zeiss Meditec Inc (R), Heidelberg Engineering (F), Heidelberg Engineering (C), Sensimed (F), Topcon (F); Christopher Bowd, None; Robert Weinreb, Alcon (F), Allergan (F), Bausch+Lomb (F), Carl Zeiss Meditec (F), Carl Zeiss Meditec (C), Carl Zeiss Meditec Inc (R), Genentech (C), Heidelberg Engineering (C), Optovue (C), Topcon (F), Topcon (C); Tu Zhuowen , None; Linda Zangwill, Carl Zeiss Meditec Inc (F), Carl Zeiss Meditec Inc (R), Heidelberg Engineering GmbH (F), Optovue Inc (R), Optovue Inc. (F), Quark (F), Topcon Medical Systems Inc. (F)
  • Footnotes
    Support  P30EY022589 Eyesight Foundation of Alabama; Alcon Laboratories Inc.; Allergan Inc.; Pfizer Inc.; Merck Inc.; Santen Inc.; and the Edith C. Blum Research Fund of the New York Glaucoma Research Institute, New York, NY, Unrestricted grant from Research to Prevent Blindness, New York, New York, P30EY022589 and participant retention incentive grants in the form of glaucoma medication at no cost from Alcon Laboratories Inc, Allergan, Pfizer Inc, and Santen Inc.Unrestricted grant from Research to Prevent Blindness, New York, New York, EY11008, EY019869, P30 EY022589, EY021818, EY025056, EY022039, EY023704, K12EY024225, Research to Prevent Blindness
Investigative Ophthalmology & Visual Science September 2016, Vol.57, No Pagination Specified. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to Subscribers Only
      Sign In or Create an Account ×
    • Get Citation

      Akram Belghith, Felipe A Medeiros, Christopher Bowd, Robert N Weinreb, Tu Zhuowen, Linda M Zangwill; A novel texture-based OCT enface image to detect and monitor glaucoma. Invest. Ophthalmol. Vis. Sci. 201657(12):.

      Download citation file:


      © 2017 Association for Research in Vision and Ophthalmology.

      ×
  • Supplements
Abstract

Purpose : To compare a novel texture-based enface image, the standard intensity-based enface image and retinal nerve fiber layer (RNFL) thickness for open-angle glaucoma (OAG) detection and to assess the ability of deep-learning convolution autoencoders to identify areas of interest from enface images and RNFL thickness maps for monitoring glaucoma.

Methods : Cross-sectional data were collected from 57 eyes of 29 healthy subjects and 181 eyes of 96 OAG patients. Longitudinal data were collected from 95 eyes (35 progressing OAG eyes and 60 healthy eyes) of 63 participants followed for an average of 3.7 years. SD-OCT optic nerve head volume and RNFL circular scans were acquired. RNFL thickness maps were generated using San Diego Automated Layer Segmentation Algorithm (SALSA). 2 OCT enface images based on the 1) average intensity and 2) tissue texture were generated from 70-μm slabs following the Inner limiting membrane. For each eye, deep-learning autoencoders were applied on the enface images and RNFL thickness map to automatically identify individualized areas of interest for OAG monitoring based on image feature ranking. Area under the receiver operating curves (AUC) was used to compare diagnostic accuracy of the circumpapillary (cp)RNFL thickness, RNFL thickness map, texture enface and intensity enface image. An eye was defined as progressing when structural loss was significantly different from zero and faster than the 5th percentile of the healthy group.

Results : For the cross-sectional and longitudinal group, healthy subjects were significantly younger than OAG subjects. The novel texture enface images and the RNFL thickness map performed significantly better than standard cpRNFL and intensity enface images for discriminating between OAG eyes (mean (± SD) visual field MD =-3.07 dB (±4.35)) and healthy eyes (age-adjusted AUC of 0.94, 0.93, 0.91 and 0.86, respectively). Among the 35 OAG progressing eyes (mean visual field MD =-7.7 dB (±4.8)), 31 (89%) eyes were identified as progressing by the RNFL thickness map, 30 (86%) eyes by texture enface image, 25 (71%) eyes by cpRNFL and 14 (40%) eyes by intensity enface image.

Conclusions : Novel texture enface images and RNFL thickness maps were significantly better than standard intensity enface images and cpRNFL thickness for discriminating between healthy and OAG eyes and for detecting progression. Deep learning methods show promise for individualizing monitoring of glaucoma.

This is an abstract that was submitted for the 2016 ARVO Annual Meeting, held in Seattle, Wash., May 1-5, 2016.

×
×

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.

×