Multivendor Spectral-Domain Optical Coherence Tomography Dataset, Observer Annotation Performance Evaluation, and Standardized Evaluation Framework for Intraretinal Cystoid Fluid Segmentation. (4th August 2016)

• Record Type: Journal Article
• Authors: Wu, Jing; Philip, Ana-Maria; Podkowinski, Dominika; Gerendas, Bianca S.; Langs, Georg; Simader, Christian; Waldstein, Sebastian M.; Schmidt-Erfurth, Ursula M.
• Other Names: Moshirfar Majid M. Academic Editor.
• Is Part Of: Journal of ophthalmology. Volume 2016(2016)
• Publication Date: 2016-08-04
• Subjects: Ophthalmology -- Periodicals; Eye Diseases; Ophthalmology; Ophthalmology; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 617.7
• DOI: 10.1155/2016/3898750
• Languages: English
• ISSNs: 2090-004X

Abstract

Abstract : Development of image analysis and machine learning methods for segmentation of clinically significant pathology in retinal spectral-domain optical coherence tomography (SD-OCT), used in disease detection and prediction, is limited due to the availability of expertly annotated reference data. Retinal segmentation methods use datasets that either are not publicly available, come from only one device, or use different evaluation methodologies making them difficult to compare. Thus we present and evaluate a multiple expert annotated reference dataset for the problem of intraretinal cystoid fluid (IRF) segmentation, a key indicator in exudative macular disease. In addition, a standardized framework for segmentation accuracy evaluation, applicable to other pathological structures, is presented. Integral to this work is the dataset used which must be fit for purpose for IRF segmentation algorithm training and testing. We describe here a multivendor dataset comprised of 30 scans. Each OCT scan for system training has been annotated by multiple graders using a proprietary system. Evaluation of the intergrader annotations shows a good correlation, thus making the reproducibly annotated scans suitable for the training and validation of image processing and machine learning based segmentation methods. The dataset will be made publicly available in the form of a segmentation Grand Challenge.