April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Automated discovery of optic nerve head structural features from image and genetic data
Author Affiliations & Notes
  • Mark Christopher
    Biomedical Engineering, University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • Li Tang
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • John H Fingert
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • Todd E Scheetz
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
  • Michael David Abramoff
    Ophthalmology and Visual Sciences, University of Iowa, Iowa City, IA
    Stephen A. Wynn Institute for Vision Research, University of Iowa, Iowa City, IA
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 4744. doi:
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    • Get Citation

      Mark Christopher, Li Tang, John H Fingert, Todd E Scheetz, Michael David Abramoff; Automated discovery of optic nerve head structural features from image and genetic data. Invest. Ophthalmol. Vis. Sci. 2014;55(13):4744.

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

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Abstract
 
Purpose
 

To discover novel associations between optic nerve head (ONH) structure, genetic factors, and disease by applying computational methods to analyze stereo fundus images and genotyping data.

 
Methods
 

Stereo fundus images captured from genotyped participants (n=1057) of the Ocular Hypertension Study were used to measure the structure of the ONH. A stereo correspondence algorithm, optimized for fundus images, was applied to the images, generating a 3D map of the ONH region for each participant. Principal component analysis (PCA) was applied to the maps to extract structural features. The relationships between ONH structural features and allelic state at several glaucoma-associated loci were then modeled using a linear discriminant approach to maximize the predictive power. This resulted in genotype-based ONH structural features representing an estimate of the contribution of each locus to ONH structure. The resulting features were evaluated based on the strength of their association with genotype and their utility in early prediction of glaucoma.

 
Results
 

The ONH structural features exhibiting strongest associations with genotype (p << 0.05) were identified for loci in the genes SIX1/SIX6, ATOH7, CDKN2B, TLR4, and ELOVL5. Incorporating these features into a model used for early prediction of glaucoma resulted in substantial increase in predictive power compared to a baseline model using only PCA-based ONH structural features.

 
Conclusions
 

The contribution of glaucoma-associated genes to ONH structure was evaluated by applying computational methods to a large dataset. By using a model that incorporated both imaging and genetic data, novel associations between phenotype and genotype were revealed. The identified ONH structural features were significantly associated with genotype and improved performance of glaucoma prediction models. Future work will examine additional genetic loci in order to further improve models used to predict and track glaucoma.

 
 
Fig 1: A: A stereo fundus pair cropped around the ONH. B: The disparity map of the stereo pair. C: A 3D rendering of the ONH structure based on the disparity map.
 
Fig 1: A: A stereo fundus pair cropped around the ONH. B: The disparity map of the stereo pair. C: A 3D rendering of the ONH structure based on the disparity map.
 
 
Fig 2: The ONH structural features most strongly associated with genotype shown with p-values indicating the strength of association. Each feature was identified using a linear discriminant approach to maximize its discriminatory power for genotypes of a glaucoma-associated locus in the indicated genes.
 
Fig 2: The ONH structural features most strongly associated with genotype shown with p-values indicating the strength of association. Each feature was identified using a linear discriminant approach to maximize its discriminatory power for genotypes of a glaucoma-associated locus in the indicated genes.
 
Keywords: 627 optic disc • 549 image processing • 629 optic nerve  
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