June 2015
Volume 56, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2015
Automated segmentation of lamina cribrosa microarchitecture from in vivo adaptive optics scanning laser ophthalmoscope images
Author Affiliations & Notes
  • Nripun Sredar
    Computer Science, University of Houston, Houston, TX
  • Kevin Ivers
    2Discoveries in Sight Research Laboratories, Devers Eye Institute, Portland, OR
  • Hope M Queener
    College of Optometry, University of Houston, Houston, TX
  • George Zouridakis
    Computer Science, University of Houston, Houston, TX
    Engineering Technology, University of Houston, Houston, TX
  • Jason Porter
    College of Optometry, University of Houston, Houston, TX
  • Footnotes
    Commercial Relationships Nripun Sredar, None; Kevin Ivers, None; Hope Queener, None; George Zouridakis, None; Jason Porter, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science June 2015, Vol.56, 1001. doi:
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      Nripun Sredar, Kevin Ivers, Hope M Queener, George Zouridakis, Jason Porter; Automated segmentation of lamina cribrosa microarchitecture from in vivo adaptive optics scanning laser ophthalmoscope images. Invest. Ophthalmol. Vis. Sci. 2015;56(7 ):1001.

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

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Abstract
 
Purpose
 

Previous quantifications of anterior lamina cribrosa surface (ALCS) pores from in vivo adaptive optics scanning laser ophthalmoscope (AOSLO) images were based on the subjective, manual marking of pore boundaries. We sought to develop an automated method to objectively segment and analyze ALCS pores and beams from AOSLO images.

 
Methods
 

In vivo AOSLO images of ALCS microarchitecture were acquired in 6 normal and 3 experimental glaucoma (EG) non-human primate eyes and 2 normal human eyes. ALCS pores were segmented using an automated hierarchical method that identified pore locations using a statistical region merging method and determined pore boundaries using an active contour level sets method. Pores were manually segmented in the same images and compared with pores segmented using the automated method. The sensitivity and specificity of the automated method was computed on a per-pixel basis dependent on whether pixels were classified as pores or beams (gold standard = manual segmentation). The repeatability of calculating ALCS pore area using automated segmentation was assessed following registration of images taken at 2 different time points (mean separation = 15 ± 7 weeks) in 3 normal eyes. Only pores that were segmented in both sessions were analyzed.

 
Results
 

The mean sensitivity and specificity of the automated method for 11 eyes were 80.9 ± 2.9% and 96.8 ± 1.6%. There were no significant differences in ALCS pore area calculated via automated segmentation at 2 different time points in 3 normal eyes (Paired t-test, P>.05). The percentage of pores identified by automated segmentation at corresponding locations in images from both sessions in 3 normal eyes were 89%, 93% and 92% (n=72, 42 and 48 pores).

 
Conclusions
 

The quantification of ALCS pores via automated segmentation was comparable to manual segmentation results and was repeatable over time. The decreased sensitivity (relative to specificity) is likely due to errors in automated segmentation of pores in areas of low contrast. Our automated segmentation method can be used to objectively characterize laminar microarchitecture in normal and glaucomatous eyes.  

 
ALCS pores after manual (yellow) and automated (blue) segmentation in (a) EG and (b) normal human eyes. Green regions show agreement. (c) Automated segmentation of ALCS pores at 2 time points (orange, violet) in a normal eye. Pink regions show agreement.
 
ALCS pores after manual (yellow) and automated (blue) segmentation in (a) EG and (b) normal human eyes. Green regions show agreement. (c) Automated segmentation of ALCS pores at 2 time points (orange, violet) in a normal eye. Pink regions show agreement.

 
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