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

Author Correction: Cross-ancestry genome-wide association analysis of corneal thickness strengthens link between complex and Mendelian eye diseases

Emmanuelle Souzeau, who contributed to analysis of data, was inadvertently omitted from the author list in the originally published version of this Article. This has now been corrected in both the PDF and HTML versions of the Article

Morphometric assessment of normal, suspect and glaucomatous optic discs with Stratus OCT and HRT II

AIMS: To compare morphometric parameters and diagnostic performance of the new Stratus Optical Coherence Tomograph (OCT) Disc mode and the Heidelberg Retina Tomograph (HRT); to evaluate OCT's accuracy in determining optic nerve head (ONH) borders. METHODS: Controls and patients with ocular hypertension, glaucoma-like discs, and glaucoma were imaged with OCT Disc mode, HRT II, and colour disc photography (DISC-PHOT). In a separate session, automatically depicted ONH shape and size in OCT were compared with DISC-PHOT, and disc borders adjusted manually where required. In a masked fashion, all print-outs and photographs were studied and discs classified as normal, borderline, and abnormal. The Cohen kappa method was then applied to test for agreement of classification. Bland-Altman analysis was used for comparison of disc measures. RESULTS: In all, 49 eyes were evaluated. Automated disc margin recognition failed in 53%. Misplaced margin points were more frequently found in myopic eyes, but only 31/187 were located in an area of peripapillary atrophy. Agreement of OCT with photography-based diagnosis was excellent in normally looking ONHs, but moderate in discs with large cups, where HRT performed better. OCT values were consistently larger than HRT values for disc and cup area. Compared with HRT, small rim areas and volumes tended to be minimized by OCT, and larger ones to be magnified. CONCLUSIONS: Stratus OCT Disc protocol performed overall well in differentiating between normal and glaucomatous ONHs. However, failure of disc border recognition was frequently observed, making manual correction necessary. ONH measures cannot be directly compared between HRT and OCT