Three-Dimensional Spectral-Domain Optical Coherence Tomography Data Analysis for Glaucoma Detection

Purpose: To develop a new three-dimensional (3D) spectral-domain optical coherence tomography (SD-OCT) data analysis method using a machine learning technique based on variable-size super pixel segmentation that efficiently utilizes full 3D dataset to improve the discrimination between early glaucomatous and healthy eyes. Methods: 192 eyes of 96 subjects (44 healthy, 59 glaucoma suspect and 89 glaucomatous eyes) were scanned with SD-OCT. Each SD-OCT cube dataset was first converted into 2D feature map based on retinal nerve fiber layer (RNFL) segmentation and then divided into various number of super pixels. Unlike the conventional super pixel having a fixed number of points, this newly developed variable-size super pixel is defined as a cluster of homogeneous adjacent pixels with variable size, shape and number. Features of super pixel map were extracted and used as inputs to machine classifier (LogitBoost adaptive boosting) to automatically identify diseased eyes. For discriminating performance assessment, area under the curve (AUC) of the receiver operating characteristics of the machine classifier outputs were compared with the conventional circumpapillary RNFL (cpRNFL) thickness measurements. Results: The super pixel analysis showed statistically significantly higher AUC than the cpRNFL (0.855 vs. 0.707, respectively, p = 0.031, Jackknife test) when glaucoma suspects were discriminated from healthy, while no significant difference was found when confirmed glaucoma eyes were discriminated from healthy eyes. Conclusions: A novel 3D OCT analysis technique performed at least as well as the cpRNFL in glaucoma discrimination and even better at glaucoma suspect discrimination. This new method has the potential to improve early detection of glaucomatous damage. © 2013 Xu et al

Identification and Assessment of Schlemm's Canal by Spectral-Domain Optical Coherence Tomography

Schlemm's canal and the collector channels were imaged with spectral domain optical coherence tomography (SD-OCT). Doppler flow measurement was used to confirm the location and identity of these structures

Retinal nerve fibre layer and visual function loss in glaucoma: the tipping point

Aims To determine the retinal nerve fibre layer (RNFL) thickness at which visual field (VF) damage becomes detectable and associated with structural loss. Methods In a prospective cross-sectional study, 72 healthy and 40 glaucoma subjects (one eye per subject) recruited from an academic institution had VF examinations and spectral domain optical coherence tomography (SD-OCT) optic disc cube scans (Humphrey field analyser and Cirrus HD-OCT, respectively). Comparison of global mean and sectoral RNFL thicknesses with VF threshold values showed a plateau of threshold values at high RNFL thicknesses and a sharp decrease at lower RNFL thicknesses. A ‘broken stick’ statistical model was fitted to global and sectoral data to estimate the RNFL thickness ‘tipping point’ where the VF threshold values become associated with the structural measurements. The slope for the association between structure and function was computed for data above and below the tipping point. Results The mean RNFL thickness threshold for VF loss was 75.3 μm (95% CI: 68.9 to 81.8), reflecting a 17.3% RNFL thickness loss from age-matched normative value. Above the tipping point, the slope for RNFL thickness and threshold value was 0.03 dB/μm (CI: −0.02 to 0.08) and below the tipping point, it was 0.28 dB/μm (CI: 0.18 to 0.38); the difference between the slopes was statistically significant (p<0.001). A similar pattern was observed for quadrant and clock-hour analysis. Conclusions Substantial structural loss (∼17%) appears to be necessary for functional loss to be detectable using the current testing methodsNational Institutes of Health (U.S.) (Grant NIH R01-EY013178)National Institutes of Health (U.S.) (Grant NIH R01-EY011289)National Institutes of Health (U.S.) (Grant 1T32EY017271)National Institutes of Health (U.S.) (Grant P30-EY08098)Research to Prevent Blindness, Inc. (United States)United States. Air Force Office of Scientific Research (FA9550-070-1-0101

Super pixel segmentation on 3D OCT images.

<p>(A) Analysis output in a healthy eye, (B) glaucoma suspect, and (C) glaucomatous eye. Abnormally thin retinal nerve fiber layer is marked with small super pixels.</p

Area under the receiver operating characteristic curves (AUCs) computed with machine classifier and Cirrus HD-OCT software generated mean cpRNFL thickness.

†<p>Difference with RNFL thickness AUC, * statistically significant.</p><p>HvGGS – healthy vs glaucoma+glaucoma suspects, HvGS – healthy vs glaucoma suspects, HvG – healthy vs glaucoma, CI – confidence interval.</p