title: Automated drusen segmentation and quantification in SD-OCT images.
abstract: Spectral domain optical coherence tomography (SD-OCT) is a useful tool for the visualization of drusen, a retinal abnormality seen in patients with age-related macular degeneration (AMD); however, objective assessment of drusen is thwarted by the lack of a method to robustly quantify these lesions on serial OCT images. Here, we describe an automatic drusen segmentation method for SD-OCT retinal images, which leverages a priori knowledge of normal retinal morphology and anatomical features. The highly reflective and locally connected pixels located below the retinal nerve fiber layer (RNFL) are used to generate a segmentation of the retinal pigment epithelium (RPE) layer. The observed and expected contours of the RPE layer are obtained by interpolating and fitting the shape of the segmented RPE layer, respectively. The areas located between the interpolated and fitted RPE shapes (which have nonzero area when drusen occurs) are marked as drusen. To enhance drusen quantification, we also developed a novel method of retinal projection to generate an en face retinal image based on the RPE extraction, which improves the quality of drusen visualization over the current approach to producing retinal projections from SD-OCT images based on a summed-voxel projection (SVP), and it provides a means of obtaining quantitative features of drusen in the en face projection. Visualization of the segmented drusen is refined through several post-processing steps, drusen detection to eliminate false positive detections on consecutive slices, drusen refinement on a projection view of drusen, and drusen smoothing. Experimental evaluation results demonstrate that our method is effective for drusen segmentation. In a preliminary analysis of the potential clinical utility of our methods, quantitative drusen measurements, such as area and volume, can be correlated with the drusen progression in non-exudative AMD, suggesting that our approach may produce useful quantitative imaging biomarkers to follow this disease and predict patient outcome.
authors.affiliation: Department of Radiology, Stanford University, Stanford, CA 94305, USA; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China. Electronic address: chen2qiang@163.com.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Chen
authors.forename: Theodore
authors.initials: T
authors.lastname: Leng
authors.forename: Luoluo
authors.initials: L
authors.lastname: Zheng
authors.forename: Lauren
authors.initials: L
authors.lastname: Kutzscher
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Ma
authors.forename: Luis
authors.initials: L
authors.lastname: de Sisternes
authors.forename: Daniel L
authors.initials: DL
authors.lastname: Rubin
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015594:Optic Disk Drusen / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity; D041623:Tomography, Optical Coherence / Q000379:methods*
keywords: AMD; Drusen segmentation; Projection image; Retinal pigment epithelium; SD-OCT