Veit Wiesmann


A4 Automated and interactive learning image-analysis for fluorescence-microscopy by the example of microbial effectors in host cells

Principal investigator
Thomas Wittenberg

Ralf Palmisano

Analysis of phase-contrast microscopy images and comparison of freely available image analysis tools

Due to the great improvements in fluorescence microscopy and the accompanying tools for interactive and automated qualitative and quantitative image analysis, classical transmitted-light microscopy information content such as interference, phase-contrast as well as polarization fade out of the scope. Nevertheless, due to their simplicity of acquisition and the important image content describing edges of cells especially phase-contrast micrographs are still an essential part of multi-modal acquired images.
Common tasks for the evaluation of cell micrographs are counting the cells, determining their size, performing intensity analysis of cell cytoplasm, or compute co- and translocalizations. Manually, these evaluations are very tedious, error-prone and time-consuming tasks, and are usually only performed on a small set of images. Hence, unbiased and reproducible as well as reliable results on large-scale data sets cannot be achieved. Therefore, appropriate segmentation methods are required which can fulfill these tasks in an effective and objective manner.
As phase-contrast micrographs are often part of multi-modal microscopy images, it is possible to obtain additional information considering them in image evaluation tasks. While cell boundaries are often hardly to identify in fluorescent micrographs, they are good observable in phase-contrast ones. Thus, it could be possible to achieve better segmentation results at cell boundaries.
Taking this into account, an adequate image processing pipeline for phase-contrast microscopy images will be developed. The use of well-known segmentation approaches such as the watershed transformation, level set segmentation including the fast marching approach as well as new algorithms, for instance graph cuts or Wavelet transform, will be explored and evaluated.
A second part of the work will be a comparison of freely available image analysis tools applicable to fluorescence microscopy images. Specifically, a criteria catalogue will be developed in cooperation with biologists in order to cover questions of the quality of the analysis, as well as practical aspects like usability and workability. The resulting overview shall give a good information which tool can be used for what type of image data, and will provide information how what criteria are necessary for the development of new tools.

Figure: Different phase-contrast micrographs of HeLa cells



Wiesmann V, Held C, Palmisano R, Wittenberg T. (2012). Segmentation of HeLa cells in phase-contrast images with and without DAPI stained cell nuclei. Biomedizinische Technik 57 (2012), Suppl. 1, pp.519-522. 

Wiesmann V, Bergler M, Palmisano R, Prinzen M, Franz D and Wittenberg T. (2014). Enhanced Fluorescent Cell Simulation using Texture Mapping and Statistical Shape Model. Biomedizinische Technik 59 (2014), Nr.s1, S.S964-S967.

Wiesmann V, Franz D, Held C, Münzenmayer C, Palmisano R and Wittenberg, T. (2014), Review of free software tools for image analysis of fluorescence cell micrographs. Journal of Microscopy.  doi: 10.1111/jmi.12184

Wiesmann V, Reimer D, Franz D, Hüttmayer H, Mielenz D and Wittenberg T. (2015). Automated high-throughput analysis of B cell spreading on immobilized antibodies with whole slide imaging. Current Directions in Biomedical Engineering 2015. doi: 10.1515/cdbme-2015-0056.

Wiesmann V, Gross C, Franz D, Thoma-Kress AK, Wittenberg T. (2016). Combining Active Contours and Active Shapes for Segmentation of Fluorescently Stained Cells. Bildverarbeitung für die Medizin 2016. Springer Berlin Heidelberg, 2016. 122-127.



July 2015 7th Annual Retreat, Erlangen School of Molecular Communication, Schloss Hirschberg, Beilngries, Germany
”Simulation and automated analysis of fluorescent cell images”
July 2014 6th Annual Retreat, Erlangen School of Molecular Communication, Kloster Banz, Bad Staffelstein, Germany
Statistical Cell Modelling
July 2013 5th Annual Retreat, Erlangen School of Molecular Communication, Kloster Banz, Bad Staffelstein, Germany
Knowledge-based Segmentation of Fluorescence Micrographs
July 2012 4th Annual Retreat, Erlangen School of Molecular Communication, Kloster Banz, Bad Staffelstein, Germany
Comparision of Graph Cuts and Watershed algorithm based on the segmentation of HeLa cells