Automated Fovea Detection in Spectral Domain Optical Coherence Tomography Scans of Exudative Macular Disease. (31st August 2016)

• Record Type: Journal Article
• Authors: Wu, Jing; Waldstein, Sebastian M.; Montuoro, Alessio; Gerendas, Bianca S.; Langs, Georg; Schmidt-Erfurth, Ursula
• Other Names: Li Chunhui Academic Editor.
• Is Part Of: International journal of biomedical imaging. Volume 2016(2016)
• Publication Date: 2016-08-31
• Subjects: Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Imagerie pour le diagnostic; Imagerie médicale; Diagnostic imaging; Imaging systems in medicine; Diagnostic Imaging -- Periodicals; Electronic journals; Periodicals; 616.0754
• DOI: 10.1155/2016/7468953
• Languages: English
• ISSNs: 1687-4188

Abstract

Abstract : In macular spectral domain optical coherence tomography (SD-OCT) volumes, detection of the foveal center is required for accurate and reproducible follow-up studies, structure function correlation, and measurement grid positioning. However, disease can cause severe obscuring or deformation of the fovea, thus presenting a major challenge in automated detection. We propose a fully automated fovea detection algorithm to extract the fovea position in SD-OCT volumes of eyes with exudative maculopathy. The fovea is classified into 3 main appearances to both specify the detection algorithm used and reduce computational complexity. Based on foveal type classification, the fovea position is computed based on retinal nerve fiber layer thickness. Mean absolute distance between system and clinical expert annotated fovea positions from a dataset comprised of 240 SD-OCT volumes was 162.3 µ m in cystoid macular edema and 262 µ m in nAMD. The presented method has cross-vendor functionality, while demonstrating accurate and reliable performance close to typical expert interobserver agreement. The automatically detected fovea positions may be used as landmarks for intra- and cross-patient registration and to create a joint reference frame for extraction of spatiotemporal features in "big data." Furthermore, reliable analyses of retinal thickness, as well as retinal structure function correlation, may be facilitated.