September 2016
Volume 57, Issue 12
Open Access
ARVO Annual Meeting Abstract  |   September 2016
Automated 3D Segmentation of Bruch's Membrane Opening from SD-OCT Volumes
Author Affiliations & Notes
  • Mohammad Saleh Miri
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
    Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, Iowa, United States
  • Michael David Abramoff
    Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
    Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, Iowa, United States
  • Young H Kwon
    Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Milan Sonka
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
    Department of Ophthalmology and Visual Sciences, The University of Iowa, Iowa City, Iowa, United States
  • Mona K Garvin
    Center for the Prevention and Treatment of Visual Loss, Iowa City VA Health Care System, Iowa City, Iowa, United States
    Department of Electrical and Computer Engineering, University of Iowa, Iowa City, Iowa, United States
  • Footnotes
    Commercial Relationships   Mohammad Saleh Miri, None; Michael Abramoff, IDx LLC (E), IDx LLC (I), University of Iowa (P); Young Kwon, None; Milan Sonka, University of Iowa (P); Mona Garvin, University of Iowa (P)
  • Footnotes
    Support  The Department of Veterans Affairs Career Development Award 1IK2RX000728; the National Institutes of Health National Eye Institute R01 EY018853 and R01 EY023279; and the Marlene S. and Leonard A. Hadley Glaucoma Research Fund
Investigative Ophthalmology & Visual Science September 2016, Vol.57, 5940. doi:
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    • Get Citation

      Mohammad Saleh Miri, Michael David Abramoff, Young H Kwon, Milan Sonka, Mona K Garvin; Automated 3D Segmentation of Bruch's Membrane Opening from SD-OCT Volumes. Invest. Ophthalmol. Vis. Sci. 2016;57(12):5940.

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

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Abstract

Purpose : Bruch's membrane opening-minimum rim width (BMO-MRW) is shown to be superior to other conventional structural parameters for diagnosing glaucoma. While most existing approaches either segment the 2D projection of BMO points or identify BMO points in individual slices, we propose a machine-learning graph based approach for 3D segmentation of BMO from glaucomatous spectral domain-optical coherence tomography (SD-OCT) volumes.

Methods : Forty-four optic-nerve-head-centered SD-OCT volumes were acquired from 44 human patients with varying stages of glaucoma using Cirrus SD-OCT (Carl Zeiss Meditec, Inc.). The BMO identification is formulated as an optimization problem for finding a 3D path within the SD-OCT volume. To this end, we transfer the volume to the radial domain such that the closed loop-path of BMO points in the Cartesian domain becomes an open-path within the radial volume. A graph-theoretic approach is utilized to find the 2D projected location of BMO points and these 2D points are projected onto the BM surface to produce the estimated location of BMO points in 3D. A machine-learning technique is employed to compute a 3D cost function around the estimated locations of BMO points. The cost function is incorporated in a dynamic programming approach to find the optimal minimum-cost path in 3D. The proposed method was compared to manual delineations (obtained by identifying the location of BMO points on 20 evenly-spaced randomly-selected B-scans manually) and the 3D iterative method proposed by Antony et al. (MICCAI 2014).

Results : Fig. 1 shows example results of BMO identification and BMO-MRW computation. The border positioning errors are reported in Table 1. The proposed method produced significantly lower unsigned and signed errors than Antony et al.’s method in the r and z directions as well as in the r–z plane (p <0.05). The proposed method, when measuring the BMO-MRW, had significantly smaller root mean square errors (RMSE) than that of Antony et al.’s method (11.62 vs. 17.99 μm).

Conclusions : The proposed method successfully identifies the 3D location of BMO points and outperformed the 3D iterative method proposed by Antony et al..

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

 

Fig. 1. Example results of the proposed method. The left column shows the original volumes and the column on the right shows the manual and automated BMO identification and the corresponding BMO-MRW measures.

Fig. 1. Example results of the proposed method. The left column shows the original volumes and the column on the right shows the manual and automated BMO identification and the corresponding BMO-MRW measures.

 

Table 1. Unsigned and signed border positioning error in (Mean ± SD).

Table 1. Unsigned and signed border positioning error in (Mean ± SD).

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