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The development of optical coherence tomography (OCT) devices has significantly influenced diagnostics and therapy guidance in ophthalmology. The growing number of available images results in the increasing importance of introducing robust algorithms for automatic segmentation in clinical practice. With advances in computer vision in recent years, development of algorithms for segmentation of the retinal structure and/or pathological biomarkers have intensified. However, we are experiencing a reproducibility crisis due to a lack of openly available databases. In this paper we give an overview of a new openly available Annotated Retinal OCT Image (AROI) database that we have developed as a result of the collaboration of one research institution and one hospital. It consists of 1136 annotated B-scans (from 24 patients suffering from age-related macular degeneration) and associated raw high-resolution images. In each B-scan, three retinal layers and three retinal fluids were annotated by an ophthalmologist. Results for intra- and inter-observer errors are obtained to set a baseline for ML algorithms validation. We believe that the AROI database offers many possibilities for the computer vision research community specialized in retinal images and represents a step towards developing a robust artificial intelligence system in ophthalmology.
annotated image database ; retinal OCT images ; automatic segmentation
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