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

Reproducibility of in-vivo OCT measured three-dimensional human lamina cribrosa microarchitecture

Purpose: To determine the reproducibility of automated segmentation of the three-dimensional (3D) lamina cribrosa (LC) microarchitecture scanned in-vivo using optical coherence tomography (OCT). Methods: Thirty-nine eyes (8 healthy, 19 glaucoma suspects and 12 glaucoma) from 49 subjects were scanned twice using swept-source (SS-) OCT in a 3.5x3.5x3.64 mm (400x400x896 pixels) volume centered on the optic nerve head, with the focus readjusted after each scan. The LC was automatically segmented and analyzed for microarchitectural parameters, including pore diameter, pore diameter standard deviation (SD), pore aspect ratio, pore area, beam thickness, beam thickness SD, and beam thickness to pore diameter ratio. Reproducibility of the parameters was assessed by computing the imprecision of the parameters between the scans. Results: The automated segmentation demonstrated excellent reproducibility. All LC microarchitecture parameters had an imprecision of less or equal to 4.2%. There was little variability in imprecision with respect to diagnostic category, although the method tends to show higher imprecision amongst healthy subjects. Conclusion: The proposed automated segmentation of the LC demonstrated high reproducibility for 3D LC parameters. This segmentation analysis tool will be useful for in-vivo studies of the LC. © 2014 Wang et al

Agreement among graders on Heidelberg retina tomograph (HRT) topographic change analysis (TCA) glaucoma progression interpretation.

PURPOSE:To evaluate agreement among experts of Heidelberg retina tomography's (HRT) topographic change analysis (TCA) printout interpretations of glaucoma progression and explore methods for improving agreement. METHODS: 109 eyes of glaucoma, glaucoma suspect and healthy subjects with 655 visits and 2 good quality HRT scans acquired at each visit were enrolled. TCA printouts were graded as progression or non-progression. Each grader was presented with 2 sets of tests: a randomly selected single test from each visit and both tests from each visit. Furthermore, the TCA printouts were classified with grader's individual criteria and with predefined criteria (reproducible changes within the optic nerve head, disregarding changes along blood vessels or at steep rim locations and signs of image distortion). Agreement among graders was modelled using common latent factor measurement error structural equation models for ordinal data. RESULTS: Assessment of two scans per visit without using the predefined criteria reduced overall agreement, as indicated by a reduction in the slope, reflecting the correlation with the common factor, for all graders with no effect on reducing the range of the intercepts between the graders. Using the predefined criteria improved grader agreement, as indicated by the narrower range of intercepts among the graders compared with assessment using individual grader's criteria. CONCLUSIONS: A simple set of predefined common criteria improves agreement between graders in assessing TCA progression. The inclusion of additional scans from each visit does not improve the agreement. We, therefore, recommend setting standardised criteria for TCA progression evaluation