Adaptive mitosis detection in large in vitro stem cell populations using timelapse microscopy

Reliable analysis of adult stem cell populations in in vitro experiments still poses a problem on the way to fully understand the regulating mechanism of these cultures. However, it is essential in the use of cultivated endogenous cells in stem cell therapies. One crucial feature during automated analysis is clearly the robust detection of mitotic events. In this work, we use the fully labeled stem cell benchmark data set CeTReS I in order to evaluate different approaches of mitosis detection: a purely time line based approach; a feature-based motility detector; and a detector based on the cell morphology changes, for which we also propose an adaptive version. We demonstrate that the approach based on morphological change outperforms the static detectors. However, the set of optimal features is changing over time, and thus it is not surprising that a feature set adapted to the systems confluency shows the best performance.