April 2011
Volume 52, Issue 14
Free
ARVO Annual Meeting Abstract  |   April 2011
Statistical Shape Analysis of the Lamina Cribrosa
Author Affiliations & Notes
  • Ian A. Sigal
    Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania
  • Jonathan L. Grimm
    Ophthalmology, University of Pittsburgh, Pittsburgh, Pennsylvania
  • John G. Flanagan
    Dept of Ophthal & Vision Sci, Univ of Toronto,Toronto Western Hosp, Toronto, Ontario, Canada
  • C R. Ethier
    Bioengineering, Imperial College London, London, United Kingdom
  • Inka Tertinegg
    Dept of Ophthal & Vision Sci, Univ of Toronto,Toronto Western Hosp, Toronto, Ontario, Canada
  • Paul Sanfilippo
    Centre for Eye Research Australia, East Melbourne, Australia
  • Footnotes
    Commercial Relationships  Ian A. Sigal, None; Jonathan L. Grimm, None; John G. Flanagan, None; C. R. Ethier, None; Inka Tertinegg, None; Paul Sanfilippo, None
  • Footnotes
    Support  Supported in part by National Institutes of Health grant P30EY008098 (Bethesda, MD); Eye and Ear Foundation (Pittsburgh, PA); and unrestricted grants from Research to Prevent Blindness (New York, NY)
Investigative Ophthalmology & Visual Science April 2011, Vol.52, 2436. doi:
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    • Get Citation

      Ian A. Sigal, Jonathan L. Grimm, John G. Flanagan, C R. Ethier, Inka Tertinegg, Paul Sanfilippo; Statistical Shape Analysis of the Lamina Cribrosa. Invest. Ophthalmol. Vis. Sci. 2011;52(14):2436.

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Abstract
 
Purpose:
 

To develop statistical models of lamina cribrosa shape (LCS).

 
Methods:
 

Lamina outlines were described with 2D elliptic Fourier analysis. The first Fourier harmonic was used to align the outlines and eliminate variation unrelated to shape. Principal component analysis of all harmonics was then employed to identify the main modes of shape variation. To test that the technique adequately captures LCS, we first analyzed a set of 143 laminas with simple and known shape variation produced from virtual sections of a generic simplified parameterized model of the optic nerve head. We then analyzed a set of 20 lamina outlines obtained from manually segmented superior-inferior histological sections of normal human eyes.

 
Results:
 

Analysis of the simplified laminas successfully identified the mean and the imposed variations in LCS (Figure). Curvature was the main mode of shape variation, accounting for 66% and 46% of shape variance in the simplified and realistic laminas, respectively. The top 5 modes accounted for 96% and 87% of shape variance in simplified and realistic laminas, respectively. The sharp corners at the lamina insertion into the sclera were only approximated by 60 harmonics.

 
Conclusions:
 

Statistical models can capture LCS and its variation. These models are more flexible than current techniques because they incorporate covariations, have minimal redundancy, and do not require defining a priori the features to extract. In the set of real laminas our analysis identified substantial superior-inferior shape variation asymmetry. To characterize lamina insertions precisely more than 60 harmonics are needed. An efficient characterization of shape may increase the sensitivity to detect changes and abnormalities.  

 
Keywords: lamina cribrosa • anatomy • computational modeling 
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