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Advances in imaging and data acquisition techniques allow for generating massive amounts of high-dimensional, multi-variate, and heterogeneous datasets in the medical, biological, as well as epidemiological domain. Particular examples are perfusion diagnostics, where 4D (3D space+time) datasets and derived parameters are analyzed in order to assess the blood flow in tissue, toponomics, where the function protein pattern in cells or tissue (the toponome) is imaged and analyzed for applications in toxicology, new drug development and patient-drug-interaction, and, population-based studies, where a cohort of people is investigated with respect to life-history and risk factors. Visual analytics provides a means for making sense of and giving insight into such highly complex data and helps in generating hypotheses. It aims at guiding the user to interesting portions of the data by incorporating his/her a priori knowledge and providing interactive filtering mechanisms. Visual Analytics merges visual exploration and data analysis techniques to reveal hidden patterns and to derive trends from the data.