June 2020
Volume 61, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2020
A method for automated Bruch’s membrane segmentation in optical coherence tomography
Author Affiliations & Notes
  • Homayoun Bagherinia
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Luis De Sisternes
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Thomas Callan
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Patricia Sha
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Maximilian Pfau
    Department of Biomedical Data Science, Stanford University, California, United States
    Department of Ophthalmology, University of Bonn, Germany
  • Laura Tracewell
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Susan Su
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Roger Goldberg
    Bay Area Retina Associates, California, United States
  • Mary K Durbin
    Carl Zeiss Meditec, Inc., Dublin, California, United States
  • Footnotes
    Commercial Relationships   Homayoun Bagherinia, Car Zeiss Meditec, Inc (E); Luis De Sisternes, Carl Zeiss Meditec, Inc. (E); Thomas Callan, Carl Zeiss Meditec, Inc. (E); Patricia Sha, Carl Zeiss Meditec, Inc. (E); Maximilian Pfau, Carl Zeiss Meditec, Inc. (F), CenterVue (F), Heidelberg Engineering (F), Optos (F); Laura Tracewell, Carl Zeiss Meditec, Inc. (C); Susan Su, Carl Zeiss Meditec, Inc. (C); Roger Goldberg, Carl Zeiss Meditec, Inc. (C); Mary Durbin, Carl Zeiss Meditec, Inc. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 489. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Homayoun Bagherinia, Luis De Sisternes, Thomas Callan, Patricia Sha, Maximilian Pfau, Laura Tracewell, Susan Su, Roger Goldberg, Mary K Durbin; A method for automated Bruch’s membrane segmentation in optical coherence tomography. Invest. Ophthalmol. Vis. Sci. 2020;61(7):489.

      Download citation file:


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

      ×
  • Supplements
Abstract

Purpose : Accurate Bruch’s membrane (BM) segmentation is essential to characterize possible choriocapillaris loss as well as elevations and dysfunctions of the retinal pigment epithelium, which are important diagnostic indicators of retinal diseases. This abstract proposes a BM segmentation method in optical coherence tomography (OCT) volumes.

Methods : The BM segmentation algorithm enhances the BM layer by using both structural (Vs) and flow (Va) OCT volumes. The enhanced OCT volume (Ve) is calculated by subtracting a proportion of mixture of structural and flow data from the structural data as Ve=Vs-α(wsVs+waVa ). ws and wa are weights and set to 0.5. α=Cov(wsVs+waVa,Vs)/Var(wsVs+waVa) assuming the similarity (squared normalized cross correlation) between Ve and the mixture (wsVs+waVa) is minimized. The segmentation algorithm is based on a multiresolution approach and a graph search algorithm. The segmentation baseline of each resolution level is used as a starting segmentation for the segmentation of the next higher resolution. The number of resolution levels is set to two for faster processing. Performance of the algorithm is evaluated by comparison to manual edits from two readers using 120 B-scans extracted from 40 OCTA cube scans of prototype 3x3 mm, 6x6 mm, 9x9 mm, 12x12 mm, and 15x9 mm acquired using 200kHz PLEX® Elite 9000 (ZEISS, Dublin, CA). All scans were mix of disease cases such as DR and AMD.

Results : Fig 1 shows two examples of enhanced OCT for BM visualization using structural and flow data, and three examples for BM segmentation with corresponding choriocapillaris vasculature maps. Fig 2 shows the mean absolute difference (including 95% confidence interval) and R2 between two readers and the readers and BM segmentation. The mean absolute difference for each scan pattern demonstrates strong correlation and great agreement between the readers and BM segmentation.

Conclusions : We proposed a new BM segmentation algorithm. Overall the automated and manual segmentations have a strong correlation and great agreement. Automated segmentation may be a valuable diagnostic tool for retinal diseases.

This is a 2020 ARVO Annual Meeting abstract.

 

Fig 1: First two rows show examples of OCT enhanced B-scans with corresponding OCT and OCT flow B-scans. Last two rows show the segmentation results with corresponding choriocapillaris slabs by using OCT flow data between BM and BM+20 µm.

Fig 1: First two rows show examples of OCT enhanced B-scans with corresponding OCT and OCT flow B-scans. Last two rows show the segmentation results with corresponding choriocapillaris slabs by using OCT flow data between BM and BM+20 µm.

 

Fig 2: Mean absolute difference and R2 between two readers and BM segmentation.

Fig 2: Mean absolute difference and R2 between two readers and BM segmentation.

×
×

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.

×