Towards a Realistic Distribution of Cells in Synthetically Generated 3D Cell Populations

  • David Svoboda
  • Vladimír Ulman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


In fluorescence microscopy, the proper evaluation of image segmentation algorithms is still an open problem. In the field of cell segmentation, such evaluation can be seen as a study of the given algorithm how well it can discover individual cells as a function of the number of them in an image (size of cell population), their mutual positions (density of cell clusters), and the level of noise. Principally, there are two approaches to the evaluation. One approach requires real input images and an expert that verifies the segmentation results. This is, however, expert dependent and, namely when handling 3D data, very tedious. The second approach uses synthetic images with ground truth data to which the segmentation result is compared objectively. In this paper, we propose a new method for generating synthetic 3D images showing naturally distributed cell populations attached to microscope slide. Cell count and clustering probability are user parameters of the method.


distance map 3D imaging cell populations cross-correlation simulation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • David Svoboda
    • 1
  • Vladimír Ulman
    • 1
  1. 1.Centre for Biomedical Image AnalysisMasaryk UniversityBrnoCzech Republic

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