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Juan Xu, Hiroshi Ishikawa, Gadi Wollstein, Richard A. Bilonick, Lindsey S. Folio, Michelle G. Sandrian, Larry Kagemann, Joel S. Schuman; A New Technique to Discriminate Glaucomatous Eyes Based on Super Pixel Machine Classifier Analysis of Three-Dimensional (3D) Spectral Domain Optical Coherence Tomography (SD-OCT) Images. Invest. Ophthalmol. Vis. Sci. 2011;52(14):3960.
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© ARVO (1962-2015); The Authors (2016-present)
To develop a 3D SD-OCT data analysis technique using super pixel machine classifier to improve glaucoma discrimination.
192 eyes of 96 subjects (44 normal, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with 3D SD-OCT (Cirrus HD-OCT; Carl Zeiss Meditec, Inc., Dublin, CA; ONH Cube 200x200 scan protocol). 400 super pixels were automatically segmented on 2D retinal nerve fiber layer (RNFL) thickness maps by grouping homogeneous neighbor pixels using our custom software (Figure). The RNFL thickness maps were adjusted by multiplying RNFL relative reflectivity. Super pixel features, i.e., average intensity value and size were used as inputs to 3 machine classifiers (boosting learning, support vector machine, and k-nearest neighbor) to automatically identify abnormal eyes. 10 fold cross validation area under the receiver operating characteristics (AUC) of the machine classifier outputs, for discriminating between normal and glaucomatous eyes, were compared with the circumpapillary RNFL thickness generated by Cirrus HD-OCT software.
Boosting learning method obtained the best AUCs amongst the machine classifiers, where the AUC of normal vs glaucoma suspect eyes was statistically significantly improved from 0.677 (the circumpapillary RNFL thickness performance) to 0.829 (p=0.018, DeLong test; Table). The AUCs did not show significant differences for all other comparisons.
A novel 3D OCT analysis technique was better at discriminating between normal and glaucoma suspect eyes than the traditional circumpapillary RNFL analysis, and performed similarly for normal vs glaucomatous eyes. This new method has the potential to improve early detection of glaucomatous damage.
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