Hjem
Universitetet i Bergen
Molecular Imaging Center

Varselmelding

There has not been added a translated version of this content. You can either try searching or go to the "area" home page to see if you can find the information there

Prof. Arvid Lundervold and Post doc Erlend Hodneland, The Neuroinformatics and Image Analysis Laboratory

CellSegm-a MATLAB toolbox for high-throughput 3D cell segmentation.

Segmentation of cells using nucleus markers in 2D. A) Raw surface stain, B)...
Segmentation of cells using nucleus markers in 2D. A) Raw surface stain, B) raw nucleus stain, C) surface stain minus nucleus stain, D) markers (blue) derived from the nucleus stain superimposed onto the surface stain, E) cell markers, F) smoothed segmentation image, from G) watershed image, H) detected cell areas.

Hovedinnhold

CellSegm, developed at Department of Biomedicine/MIC, is a Matlab based command line software toolbox providing an automated whole cell segmentation of images showing surface stained cells, acquired by fluorescence microscopy. It has options for both fully automated and semi-automated cell segmentation, and has the ability to detect various types of surface stained cells in 3D.

 Major algorithmic steps are:

(i) smoothing

(ii) Hessian-based ridge enhancement

(iii) marker-controlled watershed segmentation

(iv) feature-based classfication of cell candidates

 After detection and outlining of individual cells, the cell candidates can be subject to further analysis, specified and programmed by the end-user, or they can be analyzed by other software tools. Segmentation of tissue samples having appropriate characteristics can also be performed in CellSegm. The command-line interface of CellSegm facilitates scripting of the separate tools, all implemented in Matlab, offering a high degree of flexibility and tailored workflows for the end-user. The modularity and scripting capabilities of CellSegm enable automated workflows and quantitative analysis of microscopic data, suited for high-throughput image based screening.

For further details and code examples, see: Hodneland E, Kögel T, Frei DM, Gerdes HH, Lundervold A. Source Code Biol. Med. 2013 Aug 9;8(1):16.

18.10.2013