July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Groupwise 3D Nonlinear Registration of OCT Image Series for Analyzing Dynamic Lamina Cribrosa Changes
Author Affiliations & Notes
  • Sungmin Hong
    Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States
  • Mathilde Ravier
    Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States
  • Hiroshi Ishikawa
    NYU Langone Eye Center, NYU School of Medicine, New York, New York, United States
  • Charly Girot
    Computer Science, CPE, Lyon, France
  • Jenna Tauber
    NYU Langone Eye Center, NYU School of Medicine, New York, New York, United States
  • Gadi Wollstein
    NYU Langone Eye Center, NYU School of Medicine, New York, New York, United States
  • Joel S Schuman
    NYU Langone Eye Center, NYU School of Medicine, New York, New York, United States
  • James Fishbaugh
    Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States
  • Guido Gerig
    Computer Science and Engineering, Tandon School of Engineering, New York University, Brooklyn, New York, United States
  • Footnotes
    Commercial Relationships   Sungmin Hong, None; Mathilde Ravier, None; Hiroshi Ishikawa, None; Charly Girot, None; Jenna Tauber, None; Gadi Wollstein, None; Joel Schuman, Zeiss (P); James Fishbaugh, None; Guido Gerig, None
  • Footnotes
    Support  R01-EY013178; R01–EY011289;R01-EY025011;
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 1682. doi:
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      Sungmin Hong, Mathilde Ravier, Hiroshi Ishikawa, Charly Girot, Jenna Tauber, Gadi Wollstein, Joel S Schuman, James Fishbaugh, Guido Gerig; Groupwise 3D Nonlinear Registration of OCT Image Series for Analyzing Dynamic Lamina Cribrosa Changes. Invest. Ophthalmol. Vis. Sci. 2018;59(9):1682.

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

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Abstract

Purpose : Structural assessment of the lamina cribrosa (LC) using optical coherence tomography (OCT) is mostly reported by using 2D methods on selected cross-sections. Studies assessing dynamic changes of LC structure are typically performed with image registration in 2D which potentially skew the outcome measurements. The purpose of this study was to facilitate 3D structural assessment of dynamic LC changes on OCT images via unbiased groupwise nonlinear registration.

Methods : Two volunteers were scanned using a swept-source OCT system (Fig. a ‘Baseline’, e ‘Baseline’). For each subject, a different synthetic deformation model was applied (Fig. a, b, e, f), motivated by the assumption that pressure changes may be associated with deformations of the LC. 3D deformations between images were estimated via unbiased geometric averaging by groupwise nonlinear registration to show structural changes of LC and create a high image quality atlas from more noisy individual images. A single mask label of LC for each subject was automatically segmented on the atlas and propagated to each image in a set which reduces segmentation variability. The Wilcoxon rank-sum test was used to assess the statistical difference between simulated structural changes of different magnitudes.

Results : The estimated volume changes of LC decreased as synthetic deformations simulating pressure increased and recovered at the recovery stage (Fig. c, g). The estimated deformations for different simulated pressure stages showed statistically significant difference between pressure 1 and 2 for both experiments (p <0.01 for 99% confidence interval).

Conclusions : The proposed method showed promising results on analyzing structural changes of LC by the simulation with synthetic deformations of different types of pressure experiments. The estimated LC volume changes and deformations showed statistically significant difference with respect to pressure changes and recovery.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

Figure. Baseline images were deformed by simulated deformations (b, f; magnitudes amplified for visualization purpose) of different pressure types and magnitudes (a, e). Estimated volume changes of LC matched with simulated pressure changes (c, g). Histograms of deformation magnitudes (d, h) illustrate estimated (red) versus ground truth of simulated deformations (blue).

Figure. Baseline images were deformed by simulated deformations (b, f; magnitudes amplified for visualization purpose) of different pressure types and magnitudes (a, e). Estimated volume changes of LC matched with simulated pressure changes (c, g). Histograms of deformation magnitudes (d, h) illustrate estimated (red) versus ground truth of simulated deformations (blue).

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