Klemm, PaulLawonn, KaiRak, MarkoPreim, BernhardToennies, KlausHegenscheid, KatrinVölzke, HenryOeltze, SteffenMichael Bronstein and Jean Favre and Kai Hormann2014-02-012014-02-012013978-3-905674-51-4https://doi.org/10.2312/PE.VMV.VMV13.121-128Large-scale longitudinal epidemiological studies, such as the Study of Health in Pomerania (SHIP), investigate thousands of individuals with common characteristics or experiences (a cohort) including a multitude of sociodemographic and biological factors. Unique for SHIP is the inclusion of medical image data acquired via an extensive whole-body MRI protocol. Based on this data, we study the variability of the lumbar spine and its relation to a subset of socio-demographic and biological factors. We focus on the shape of the lumbar spinal canal which plays a crucial role in understanding the causes of lower back pain. We propose an approach for the reproducible analysis of lumbar spine canal variability in a cohort. It is based on the centerline of each individual canal, which is derived from a semi-automatic, model-based detection of the lumbar spine. The centerlines are clustered by means of Agglomerative Hierarchical Clustering to form groups with low intra-group and high inter-group shape variability. The number of clusters is computed automatically. The clusters are visualized by means of representatives to reduce visual clutter and simplify a comparison between subgroups of the cohort. Special care is taken to convey the shape of the spinal canal also orthogonal to the view plane.We demonstrate our approach for 490 individuals drawn from the SHIP data.We present preliminary results of investigating the clusters with respect to their associated socio-demographic and biological factors.J.3 [Computer Applications]Life and Medical SciencesHealth