Towards a Realistic Distribution of Cells in Synthetically Generated 3D Cell Populations
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.
Keywordsdistance map 3D imaging cell populations cross-correlation simulation
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