Segmentation of the Optic Disc in 3-D OCT Scans of the Optic Nerve Head

Abstract

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 interobserver variability.

Retinal Imaging and Image Analysis

“…33 and 34 demonstrate the performance of this approach. Overall, the approach reported in [165] achieved results not significantly different ( p > 0.2) from the inter-observer variability of expert-analysis of the ONH cup and rim boundaries. In 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), the unsigned errors for the optic disc cup and neuroretinal rim were 2.52 ± 0.87 pixels (0.076 ± 0.026 mm) and 2.04 ± 0.86 pixels (0.061 ± 0.026 mm), respectively.…”

“…For each voxel column, the classifier determines k nearest neighbors in the feature space and assigns the most common label amongst the nearest neighbors to the query voxel column (Fig. 33) [165]. …”

Avoiding Clinical Misinterpretation and Artifacts of Optical Coherence Tomography Analysis of the Optic Nerve, Retinal Nerve Fiber Layer, and Ganglion Cell Layer

“…The problem can be theoretically overcome by analyzing the inner layers of the macula, specifically the GCL-IPL complex ( 60 ). Unfortunately, the commercially available segmentation algorithms are prone to segmentation failures of the GCL-IPL complex, especially when there is low signal strength, optic nerve edema, or structural abnormalities in the outer retinal layers (which affects segmentation of the inner retinal layers) ( 61–63 ). One sign of inaccurate inner layer segmentation is the appearance of nonpathologic shapes in the thickness and probability maps, such as a corner of abnormal thinning (Figs.…”

Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

“… Figure 14 shows a comparison between the segmented layers of a 650 × 512 × 128 Topcon 3D OCT-1000 imaging system using proposed method in [53]. It is clear that the first layer is detected truly after despeckling.…”

“…A comparison between the segmented layers of a 650 × 512 × 128 Topcon 3D OCT-1000 imaging system using proposed method in [53]. From left to right: original image, denoised image by nonlocal homomorphic BiGaussRayMixShrinkL method, and local homomorphic BiGaussRayMixShrinkL method.…”