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Fluorescence microscopy has regained much attention in the last years especially in the field of systems biology. It has been recognized as a rich source of information extending the existing sources since it allows simultaneous collection of spatial and temporal protein information. In order to enable a high-throughput and high-content image analysis, sophisticated image processing routines become essential. We present a machine learning based approach for semantic image annotation i.e. identifying biologically meaningful objects. A semantic annotation becomes necessary, if image variables have to be associated to single biological objects, for example cells. We apply our method to pancreatic tissue sample images to detect and annotate cells of the Islets of Langerhans and whole pancreas. Based on the annotation, aligned multichannel fluorescence images are evaluated for cell type classification allowing accurate and rapid determination of the cell number and mass. This high-throughput analytical technique, requiring only few parameters, should be of great value in diabetes studies and for screening of new anti-diabetes treatments.