Multidimensional imaging techniques provide powerful ways to examine various kinds of
scientific questions. The routinely produced data sets in the terabyte-range however can
hardly be analyzed manually and require an extensive use of automated image analysis. The
present work introduces a new concept for the estimation and propagation of uncertainty
involved in image analysis operators and new segmentation algorithms that are suitable for
terabyte-scale analyses of 3D+t microscopy images.
