June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
OCT-OCTA Segmentation: a Novel Framework and an Application to Segment Bruch's Membrane in the Presence of Drusen
Author Affiliations & Notes
  • Julia Schottenhamml
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
    Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
  • Eric M. Moult
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
  • Eduardo Amorim Novais
    Department of Ophthalmology, Federal University of Sao Paulo, Sao Paulo, Brazil
    New England Eye Center, Tufts Medical Center, Boston, Massachusetts, United States
  • Martin F Kraus
    Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
  • ByungKun Lee
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
  • WooJhon Choi
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
  • Stefan B Ploner
    Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
  • Lennart Husvogt
    Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
  • Chen D Lu
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
  • Patrick Yiu
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
  • Philip J Rosenfeld
    Department of Ophthalmology, University of Miami Miller School of Medicine, Miami, Florida, United States
  • Jay S Duker
    New England Eye Center, Tufts Medical Center, Boston, Massachusetts, United States
  • Andreas K Maier
    Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany
  • Nadia Waheed
    New England Eye Center, Tufts Medical Center, Boston, Massachusetts, United States
  • James G Fujimoto
    Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Julia Schottenhamml, None; Eric Moult, None; Eduardo Novais, None; Martin Kraus, Optovue, Inc. (C), Optovue, Inc. (P); ByungKun Lee, None; WooJhon Choi, None; Stefan Ploner, None; Lennart Husvogt, None; Chen Lu, None; Patrick Yiu, None; Philip Rosenfeld, Carl Zeiss Meditec, Inc. (F), Carl Zeiss Meditec, Inc. (C), Carl Zeiss Meditec, Inc. (R); Jay Duker, Carl Zeiss Meditec, Inc. (F), Carl Zeiss Meditec, Inc. (C), Optovue, Inc. (F), Optovue, Inc. (C), Topcon Medical Systems, Inc. (F), Topcon Medical Systems, Inc. (C); Andreas Maier, None; Nadia Waheed, Carl Zeiss Meditec, Inc. (R), Genentech (C), Janssen (C), MVRF (F), Nidek (R), Ocudyne (C), Optovue, Inc. (R), Regeneron (C); James Fujimoto, Carl Zeiss Meditec, Inc. (P), Optovue, Inc. (P), Optovue, Inc. (I)
  • Footnotes
    Support  NIH Grant 5-R01-EY011289-28, AFOSR Grant FA9550-15-1-0473, AFOSR Grant FA9550-10-1-0551
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 645. doi:
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      Julia Schottenhamml, Eric M. Moult, Eduardo Amorim Novais, Martin F Kraus, ByungKun Lee, WooJhon Choi, Stefan B Ploner, Lennart Husvogt, Chen D Lu, Patrick Yiu, Philip J Rosenfeld, Jay S Duker, Andreas K Maier, Nadia Waheed, James G Fujimoto; OCT-OCTA Segmentation: a Novel Framework and an Application to Segment Bruch's Membrane in the Presence of Drusen. Invest. Ophthalmol. Vis. Sci. 2017;58(8):645.

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

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Abstract

Purpose : We present a novel framework for segmenting optical coherence tomography (OCT) and OCT angiography (OCTA) that jointly uses structural and angiographic information. We term this new paradigm “OCT-OCTA segmentation,” and demonstrate its utility by segmenting Bruch’s membrane (BM) in the presence of drusen.

Methods : We developed an automatic OCT-OCTA graph-cut algorithm for BM segmentation. Our algorithm’s performance was quantitatively validated by comparing it with manual segmentation in 7 eyes (6 patients; 73.8±5.7 y/o) with drusen. The algorithm was also qualitatively assessed in healthy eyes (n=13), eyes with diabetic retinopathy (n=21), early/intermediate age-related macular degeneration (AMD) (n=14), exudative AMD (n=5), geographic atrophy (GA) (n=6), and polypoidal choroidal vasculopathy (n=7).

Results : The absolute pixel-wise error between the manual and automatic segmentations had the following values: mean: 4.5±0.89um; 1st Quartile: 1.9±1.35um; 2nd Quartile: 3.9±1.90um; and 3rd Quartile: 6.3±2.67. This corresponds to a mean absolute error smaller than the optical axial resolution of our OCT system (~8-9um). In all other tested eyes, qualitative visual inspection showed BM contours that were deemed suitably accurate for use in forming en face OCT(A) projections. The algorithm’s poorest results occurred in GA patients with large areas of atrophy.

Conclusions : By leveraging both structural and angiographic information we showed that OCT-OCTA segmentation is likely to be a widely useful framework for segmenting ocular structures.

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

 

En face segmentation analysis, where each column corresponds to a different nGA/DAGA eye. 1st row: color fundus photo; 2nd, 3rd rows: en face OCTA slice taken at the manual and automatic segmentations, respectively; 4th, 5th row: en face OCT slices taken the manual and automatic segmentations, respectively. 6th row: heat map of the segmentation error (legend, bottom right; units of pixels; 1 pixel = 4.5um). All OCT(A) fields are 6x6mm.

En face segmentation analysis, where each column corresponds to a different nGA/DAGA eye. 1st row: color fundus photo; 2nd, 3rd rows: en face OCTA slice taken at the manual and automatic segmentations, respectively; 4th, 5th row: en face OCT slices taken the manual and automatic segmentations, respectively. 6th row: heat map of the segmentation error (legend, bottom right; units of pixels; 1 pixel = 4.5um). All OCT(A) fields are 6x6mm.

 

OCT(A) B-scan analysis of manual (teal) and automatic (orange) segmentations; A-D are taken from the white lines in Figure 1. Each row shows both OCT data (left) and OCTA data (right). Enlargements, indicated by red and green boxes, are also shown.

OCT(A) B-scan analysis of manual (teal) and automatic (orange) segmentations; A-D are taken from the white lines in Figure 1. Each row shows both OCT data (left) and OCTA data (right). Enlargements, indicated by red and green boxes, are also shown.

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