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Cell tracking is a crucial component of many biomedical image analysis applications. Many available cell tracking systems assume high precision of the cell detection module. Therefore low performance in cell detection can heavily affect the tracking results. Unfortunately cell segmentation modules often have significant errors, especially in the case of phase-contrast imaging. In this paper we propose a tracking method that does not rely on perfect cell segmentation and can deal with uncertainties by exploiting temporal information and aggregating the results of many frames. Our tracking algorithm is fully automated and can handle common challenges of tracking such as cells entering/exiting the screen and mitosis events. To handle the latter, we modify the standard flow network and introduce the concept of a splitting node into it. Experiment results show that adding temporal information from the video microscopy improves the cell/mitosis detection and results in a better tracking system.