title: Automated segmentation of optic disc in SD-OCT images and cup-to-disc ratios quantification by patch searching-based neural canal opening detection.
abstract: Glaucoma is one of the most common causes of blindness worldwide. Early detection of glaucoma is traditionally based on assessment of the cup-to-disc (C/D) ratio, an important indicator of structural changes to the optic nerve head. Here, we present an automated optic disc segmentation algorithm in 3-D spectral domain optical coherence tomography (SD-OCT) volumes to quantify this ratio. The proposed algorithm utilizes a two-stage strategy. First, it detects the neural canal opening (NCO) by finding the points with maximum curvature on the retinal pigment epithelium (RPE) boundary with a spatial correlation smoothness constraint on consecutive B-scans, and it approximately locates the coarse disc margin in the projection image using convex hull fitting. Then, a patch searching procedure using a probabilistic support vector machine (SVM) classifier finds the most likely patch with the NCO in its center in order to refine the segmentation result. Thus, a reference plane can be determined to calculate the C/D radio. Experimental results on 42 SD-OCT volumes from 17 glaucoma patients demonstrate that the proposed algorithm can achieve high segmentation accuracy and a low C/D ratio evaluation error. The unsigned border error for optic disc segmentation and the evaluation error for C/D ratio comparing with manual segmentation are 2.216 ± 1.406 pixels (0.067 ± 0.042 mm) and 0.045 ± 0.033, respectively.
authors.forename: Menglin
authors.initials: M
authors.lastname: Wu
authors.forename: Theodore
authors.initials: T
authors.lastname: Leng
authors.forename: Luis
authors.initials: L
authors.lastname: de Sisternes
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
authors.forename: Qiang
authors.initials: Q
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000473:pathology; D005901:Glaucoma / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D054259:Neural Tube / Q000473:pathology*; D009898:Optic Disk / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D041623:Tomography, Optical Coherence / Q000379:methods*