In biomedical image processing, correct tracking of individual cells is important task for the study of dynamic cellular processes. It is, however, often difficult to decide whether obtained tracking results are correct or not. This is mainly due to complexity of the data that can show hundreds of cells, due to improper data sampling either in time or in space, or when the time-lapse sequence consists of blurred noisy images. This prohibits manual extraction of reliable ground truth (GT) data as well. Nonetheless, if reliable testing data with GT were available, one could compare the results of the examined tracking algorithm with the GT and assess its performance quantitatively.In this paper, we introduce a novel versatile tool capable of generating 2D image sequences showing simulated living cell populations with GT for evaluation of biomedical tracking. The simulated events include namely cell motion, cell division, and cell clustering up to tissue-level density. The method is primarily designed to operate at inter-cellular scope.

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