Coupled Minimum-Cost Flow Cell Tracking

  • Dirk Padfield
  • Jens Rittscher
  • Badrinath Roysam
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5636)


A growing number of screening applications require the automated monitoring of cell populations in a high-throughput, high-content environment. These applications depend on accurate cell tracking of individual cells that display various behaviors including mitosis, occlusion, rapid movement, and entering and leaving the field of view. We present a tracking approach that explicitly models each of these behaviors and represents the association costs in a graph-theoretic minimum-cost flow framework. We show how to extend the minimum-cost flow algorithm to account for mitosis and merging events by coupling particular edges. We applied the algorithm to nearly 6,000 images of 400,000 cells representing 32,000 tracks taken from five separate datasets, each composed of multiple wells.Our algorithm is able to track cells and detect different cell behaviors with an accuracy of over 99%.


Tracking minimum-cost flow cell analysis graph-theoretic segmentation wavelets quantitative analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Paragios, N., Deriche, R.: Geodesic active regions for motion estimation and tracking. In: ICCV, vol. 1, pp. 688–694 (1999)Google Scholar
  2. 2.
    Blake, A., Isard, M.: Active Contours. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Yang, F., Mackey, M., Ianzini, F., Gallardo, G., Sonka, M.: Cell segmentation, tracking, and mitosis detection using temporal context. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 302–309. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Padfield, D., Rittscher, J., Thomas, N., Roysam, B.: Spatio-temporal cell cycle phase analysis using level sets and fast marching methods. Med. I.A (2008)Google Scholar
  5. 5.
    Dufour, A., Shinin, V., Tajbakhsh, S., Guillen-Aghion, N., Olivo-Marin, J., Zimmer, C.: Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces. TIP 14(9) (2005)Google Scholar
  6. 6.
    Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: CVPR, vol. 2, pp. 142–149 (2000)Google Scholar
  7. 7.
    Debeir, O., Van Ham, P., Kiss, R., Decaestecker, C.: Tracking of migrating cells under phase-contrast video microscopy with combined mean-shift processes. TMI 24(6), 697–711 (2005)Google Scholar
  8. 8.
    Gelb, A. (ed.): Applied Optimal Estimation. MIT Press, Cambridge (1979)Google Scholar
  9. 9.
    Blake, A., Isard, M.: Condensation – conditional density propagation for visual tracking. IJCV 28(1), 5–28 (1998)CrossRefGoogle Scholar
  10. 10.
    Li, K., Miller, E., Chen, M., Kanade, T., Weiss, L., Campbell, P.: Cell population tracking and lineage construction with spatiotemporal context. Med. I.A. 12(5), 546–566 (2008)Google Scholar
  11. 11.
    Genovesio, A., Liedl, T., Emiliani, V., Parak, W.J., Coppey-Moisan, M., Olivo-Marin, J.: Multiple particle tracking in 3-d+t microscopy: method and application to the tracking of endocytosed quantum dots. TIP 15(5), 1062–1070 (2006)Google Scholar
  12. 12.
    Kachouie, N., Fieguth, P., Ramunas, J., Jervis, E.: Probabilistic model-based cell tracking. International Journal of Biomedical Imaging, 1–10 (2006)Google Scholar
  13. 13.
    Al-Kofahi, O., Radke, R.J., Goderie, S.K., Shen, Q., Temple, S., Roysam, B.: Automated cell lineage construction: a rapid method to analyze clonal development established with murine neural progenitor cells. Cell Cycle 5(3), 327–335 (2006)CrossRefGoogle Scholar
  14. 14.
    De Hauwer, C., Darro, F., Camby, I., Kiss, R., Van Ham, P., Decaesteker, C.: In vitro motility evaluation of aggregated cancer cells by means of automatic image processing. Cytometry 36(1), 1–10 (1999)CrossRefGoogle Scholar
  15. 15.
    Padfield, D., Rittscher, J., Roysam, B.: Spatio-temporal cell segmentation and tracking for automated screening. In: IEEE ISBI (2008)Google Scholar
  16. 16.
    Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: CVPR (2008)Google Scholar
  17. 17.
    Sbalzarini, I.F., Koumoutsakos, P.: Feature point tracking and trajectory analysis for video imaging in cell biology. Journal of Structural Biology 151(2), 182–195 (2005)CrossRefGoogle Scholar
  18. 18.
    Cormen, T., Leiserson, C., Rivest, R., Stein, C.: Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2001)MATHGoogle Scholar
  19. 19.
    Olivo-Marin, J.: Automatic detection of spots in biological images by a wavelet-based selective filtering technique. In: ICIP, pp. I: 311–314 (1996)Google Scholar
  20. 20.
    Padfield, D., Rittscher, J., Roysam, B.: Defocus and low CNR detection for cell tracking applications. In: MICCAI MIAAB Workshop (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Dirk Padfield
    • 1
    • 2
  • Jens Rittscher
    • 1
  • Badrinath Roysam
    • 2
  1. 1.GE Global ResearchOne Research CircleNiskayunaUSA
  2. 2.Rensselaer Polytechnic InstituteTroyUSA

Personalised recommendations