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Glaucoma is the second leading ocular disease causing blindness due to gradual damage to the optic nerve and resultant visual field loss. Segmentations of the optic disc cup and neuroretinal rim can provide important parameters for detecting and tracking this disease. The purpose of this study is to describe and evaluate a method that can automatically segment the optic disc cup and rim in spectral-domain 3-D OCT (SD-OCT) volumes. Four intraretinal surfaces were segmented using a fast multiscale 3-D graph search algorithm. After surface segmentation, the retina in each 3-D OCT scan was flattened to ensure a consistent optic nerve head shape. A set of 15 features, derived from the segmented intraretinal surfaces and voxel intensities in the SD-OCT volume, were used to train a classifier that can determine which A-scans in the OCT volume belong to the background, optic disc cup and rim. Finally, prior knowledge about the shapes of the cup and rim was incorporated into the system using a convex hull-based approach. Two glaucoma experts annotated the cup and rim area using planimetry, and the annotations of the first expert were used as the reference standard. A leave-one-subject-out experiment on 27 optic nerve head-centered OCT volumes (14 right eye scans and 13 left eye scans from 14 patients) was performed. Two different types of classification methods were compared, and experimental results showed that the best performing method had an unsigned error for the optic disc cup of 2.52 ? 0.87 pixels (0.076 ? 0.026 mm) and for the neuroretinal rim of 2.04 ? 0.86 pixels (0.061 ? 0.026 mm). The interobserver variability as indicated by the unsigned border positioning difference between the second expert observer and the reference standard was 2.54 ? 1.03 pixels (0.076 ? 0.031 mm for the optic disc cup and 2.14 ? 0.80 pixels (0.064 ? 0.024 mm for the neuroretinal rim. The unsigned error of the best performing method was not significantly different (p > 0.2) from the in- erobserver variability.