Combining active contours and active shapes for segmentation of fluorescently stained cells

Fluorescence microscopy is an essential tool to examine host-pathogen interactions such as the influence of Fascin on cell-cell contacts between infected and uninfected cells. Manual analysis of fluorescence microscopy images is prone to errors leading to inter- and intra-observer variability. To increase reproducibility and objectivity, automated and semi-automated image processing methods are required. For a reliable segmentation of touching and overlapping cells, we propose an active contours algorithm extended by an energy term based on an active shape model. The algorithm is evaluated on confocal cell image data labeled by a human expert.