Computer vision tracking of stemness

Clinical translation of stem cell research promises to revolutionize medicine. Challenges remain toward belter understanding of stem cell biology and cost-effective strategies for stem cell manufacturing. These challenges call for novel engineering toolsets to study stem cell behaviors and the associated sternness. Towards this goal, we are developing a computer vision based system to automatically and reliably follow the behaviors of individual stem cells in expanding populations. This paper reports on significant progress in our development. In particular, we present a machine-learning approach for detecting spatiotemporal mitosis events without image segmentation. This approach not only improves tracking performance, but can also independently quantify mitoses and cellular divisions. We also employ bilateral filtering to improve cell detection performance. We demonstrate the effectiveness of this system on tracking C2C12 mouse myoblast stem cells.