Computer vision tracking of stemness
Kang Li, Eric David Miller, Mei Chen, Takeo Kanade, Lee E. Weiss, Phil G. Campbell · 2008
Clinical translation of stem cell research promises to revolutionize medicine. Challenges remain toward better 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 stemness. 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. Index Terms — Computer vision, tracking, time-lapsed microscopy, stem cells, stemness