Automatic cell segmentation in strongly agglomerated cell networks for different cell types. (1st January 2014)

• Record Type: Journal Article
• Authors: Buhl, S.; Neumann, B.; Schäfer, S.C.; Severing, A.L.
• Is Part Of: International journal of computational biology and drug design. Volume 7:Number 2/3(2014)
• Page Start: 259
• Page End: 277
• Publication Date: 2014-01-01
• Subjects: automatic cell segmentation -- cell segmentation -- cell cluster separation -- cluster splitting -- growth simulation -- biocompatibility testing; Computational biology -- Periodicals; Drugs -- Design -- Periodicals; 570.285
• Languages: English
• ISSNs: 1756-0756

Abstract

This paper presents a method of separating cells that are connected to each other forming clusters. The difference to many other publications covering similar topics is that the cell types we are dealing with form clusters of highly varying morphology. An advantage of our method is that it can be universally used for different cell types. The segmentation method is based on a growth simulation starting from the nuclei areas. To start the evaluation, the cells need to be made visible with a histological stain, in our case with the May-Grünwald solution. After the staining process has been completed, the nuclei areas can be distinguished from the other cell areas by a histogram backprojection algorithm. The presented method can, in addition to histological stained cells, also be applied to fluorescent-stained cells.