title: Using multimodal information for the segmentation of fluorescent micrographs with application to virology and microbiology.
abstract: In order to improve reproducibility and objectivity of fluorescence microscopy based experiments and to enable the evaluation of large datasets, flexible segmentation methods are required which are able to adapt to different stainings and cell types. This adaption is usually achieved by the manual adjustment of the segmentation methods parameters, which is time consuming and challenging for biologists with no knowledge on image processing. To avoid this, parameters of the presented methods automatically adapt to user generated ground truth to determine the best method and the optimal parameter setup. These settings can then be used for segmentation of the remaining images. As robust segmentation methods form the core of such a system, the currently used watershed transform based segmentation routine is replaced by a fast marching level set based segmentation routine which incorporates knowledge on the cell nuclei. Our evaluations reveal that incorporation of multimodal information improves segmentation quality for the presented fluorescent datasets.
authors.affiliation: Department of image Processing, Fraunhofer-Institute for Integrated Circuits, Erlangen, Germany.
authors.forename: Christian
authors.initials: C
authors.lastname: Held
authors.forename: Jens
authors.initials: J
authors.lastname: Wenzel
authors.forename: Rike
authors.initials: R
authors.lastname: Webel
authors.forename: Manfred
authors.initials: M
authors.lastname: Marschall
authors.forename: Roland
authors.initials: R
authors.lastname: Lang
authors.forename: Ralf
authors.initials: R
authors.lastname: Palmisano
authors.forename: Thomas
authors.initials: T
authors.lastname: Wittenberg
mesh_terms: D000465:Algorithms; D000818:Animals; D002467:Cell Nucleus / Q000378:metabolism; D019295:Computational Biology / Q000379:methods; D001330:Electronic Data Processing; D006367:HeLa Cells; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008264:Macrophages / Q000166:cytology / Q000378:metabolism; D051379:Mice; D008828:Microbiological Techniques; D008856:Microscopy, Fluorescence / Q000379:methods*; D015233:Models, Statistical; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results