Cell Population Tracking and Lineage Construction with Spatiotemporal Context

  • Kang Li
  • Mei Chen
  • Takeo Kanade
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4792)


Automated visual-tracking of cell populations in vitro using phase contrast time-lapse microscopy is vital for quantitative, systematic and high-throughput measurements of cell behaviors. These measurements include the spatiotemporal quantification of migration, mitosis, apoptosis, and cell lineage. This paper presents an automated cell tracking system that can simultaneously track and analyze thousands of cells. The system performs tracking by cycling through frame-by-frame track compilation and spatiotemporal track linking, combining the power of two tracking paradigms. We applied the system to a range of cell populations including adult stem cells. The system achieved tracking accuracies in the range of 83.8%–92.5%, outperforming previous work by up to 8%.


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  1. 1.
    Li, K., Miller, E.D., Weiss, L.E., Campbell, P.G., Kanade, T.: Online tracking of migrating and proliferating cells imaged with phase-contrast microscopy. In: Proc. IEEE Conf. Comp. Vision and Patt. Recog. Workshop, p. 65. IEEE Computer Society Press, Los Alamitos (2006)Google Scholar
  2. 2.
    Blom, H.A.P.: An efficient filter for abruptly changing systems. In: Proc. 23rd IEEE Conference on Decision and Control, pp. 656–658. IEEE Computer Society Press, Los Alamitos (1984)Google Scholar
  3. 3.
    Genovesio, A., Liedl, T., Emiliani, V., Parak, W.J., Coppey-Moisan, M., Olivo-Marin, J.C.: Multiple particle tracking in 3-D+t microscopy: Method and application to the tracking of endocytosed quantum dots. IEEE Transactions of Medical Imaging 15, 1062–1070 (2006)CrossRefGoogle Scholar
  4. 4.
    Shi, Y., Karl, W.C.: Real-time tracking using level sets. In: Proc. IEEE Conf. Comp. Vision and Patt. Recog., vol. 2, pp. 34–41. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  5. 5.
    Shumway, R., Stoffer, D.: An approach to time series smoothing and forecasting using the EM algorithm. Journal of Time Series Analysis 3, 253–264 (1982)MATHGoogle Scholar
  6. 6.
    Jonker, R., Volgenant, A.: A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing 38, 325–340 (1987)MATHCrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Kang Li
    • 1
  • Mei Chen
    • 2
  • Takeo Kanade
    • 1
  1. 1.Carnegie Mellon University 
  2. 2.Intel Research Pittsburgh 

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