Röhlig, MartinRosenthal, PaulSchmidt, ChristophSchumann, HeidrunStachs, OliverMichael Sedlmair and Christian Tominski2017-06-122017-06-122017978-3-03868-042-0https://doi.org/10.2312/eurova.20171117https://diglib.eg.org:443/handle/10.2312/eurova20171117Optical coherence tomography (OCT) enables noninvasive high-resolution 3D imaging of the human retina and thus, plays a fundamental role in detecting a wide range of ocular diseases. Despite OCT's diagnostic value, managing and analyzing resulting data is challenging. We apply two visual analytics strategies for supporting retinal assessment in practice. First, we provide an interface for unifying and structuring data from different sources into a common basis. Fusing that basis with medical records and augmenting it with analytically derived information facilitates thorough investigations. Second, we present a tailored visual analysis tool for presenting, selecting, and emphasizing different aspects of the attributed data. This enables free exploration, reducing the data to relevant subsets, and focusing on details. By applying both strategies, we effectively enhance the management and the analysis of OCT data for assisting medical diagnoses.Humancentered computingVisualizationVisualization application domainsVisual analytics10.2312/eurova.2017111737-41