A Discrete Chain Graph Model for 3d+t Cell Tracking with High Misdetection Robustness

  • Bernhard X. Kausler
  • Martin Schiegg
  • Bjoern Andres
  • Martin Lindner
  • Ullrich Koethe
  • Heike Leitte
  • Jochen Wittbrodt
  • Lars Hufnagel
  • Fred A. Hamprecht
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)

Abstract

Tracking by assignment is well suited for tracking a varying number of divisible cells, but suffers from false positive detections. We reformulate tracking by assignment as a chain graph–a mixed directed-undirected probabilistic graphical model–and obtain a tracking simultaneously over all time steps from the maximum a-posteriori configuration. The model is evaluated on two challenging four-dimensional data sets from developmental biology. Compared to previous work, we obtain improved tracks due to an increased robustness against false positive detections and the incorporation of temporal domain knowledge.

Keywords

chain graph graphical model cell tracking 

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References

  1. 1.
    Andres, B., Kappes, J.H., Köthe, U., Schnörr, C., Hamprecht, F.A.: An Empirical Comparison of Inference Algorithms for Graphical Models with Higher Order Factors Using OpenGM. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds.) DGAM 2010. LNCS, vol. 6376, pp. 353–362. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Arulampalam, M., Maskell, S., Gordon, N., Clapp, T.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing 50(2), 174–188 (2002)CrossRefGoogle Scholar
  3. 3.
    Bise, R., Yin, Z., Kanade, T.: Reliable cell tracking by global data association. In: IEEE International Symposium on Biomedical Imaging, ISBI 2011 (2011)Google Scholar
  4. 4.
    Blake, A., Kohli, P., Rother, C. (eds.): Markov Random Fields for Vision and Image Processing. MIT Press (2011)Google Scholar
  5. 5.
    Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)MATHCrossRefGoogle Scholar
  6. 6.
    Brendel, W., Amer, M., Todorovic, S.: Multiobject tracking as maximum weight independent set. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011), pp. 1273–1280 (2011)Google Scholar
  7. 7.
    Doucet, A., Johansen, A.: A tutorial on particle filtering and smoothing: Fifteen years later. In: Handbook of Nonlinear Filtering, Oxford University Press (2011)Google Scholar
  8. 8.
    Fox, E., Choi, D., Willsky, A.: Nonparametric Bayesian methods for large scale multi-target tracking. In: Fortieth Asilomar Conference on Signals, Systems and Computers, ACSSC 2006, pp. 2009–2013 (2006)Google Scholar
  9. 9.
    Frydenberg, M.: The chain graph Markov property. Scandinavian Journal of Statistics 17, 333–353 (1990)MathSciNetMATHGoogle Scholar
  10. 10.
    Jiang, H., Fels, S., Little, J.J.: A linear programming approach for multiple object tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007 (2007)Google Scholar
  11. 11.
    Kachouie, N.N., Fieguth, P.W.: Extended-Hungarian-JPDA: exact single-frame stem cell tracking. IEEE Transactions on Bio-Medical Engineering 54 (2007)Google Scholar
  12. 12.
    Kanade, T., Yin, Z., Bise, R., Huh, S., Eom, S., Sandbothe, M.F., Chen, M.: Cell image analysis: Algorithms, system and applications. In: IEEE Workshop on Applications of Computer Vision (WACV) (2011)Google Scholar
  13. 13.
    Keller, P.J., Schmidt, A.D., Wittbrodt, J., Stelzer, E.H.: Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008)CrossRefGoogle Scholar
  14. 14.
    Kschischang, F., Frey, B., Loeliger, H.A.: Factor graphs and the sum-product algorithm. IEEE Transactions on Information Theory 47, 498–519 (2001)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Li, Y., Huang, C., Nevatia, R.: Learning to associate: HybridBoosted multi-target tracker for crowded scene. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 (2009)Google Scholar
  16. 16.
    Lou, X., Kaster, F.O., Lindner, M.S., Kausler, B.X., Koethe, U., Jaenicke, H., Hoeckendorf, B., Wittbrodt, J., Hamprecht, F.A.: DELTR: Digital embryo lineage tree reconstructor. In: IEEE International Symposium on Biomedical Imaging, ISBI 2011 (2011)Google Scholar
  17. 17.
    Meijering, E., Dzyubachyk, O., Smal, I., van Cappellen, W.A.: Tracking in cell and developmental biology. Seminars in Cell & Developmental Biology 20 (2009)Google Scholar
  18. 18.
    Melani, C., Peyrieras, N., Mikula, K., Zanella, C., Campana, M., Rizzi, B., Veronesi, F., Sarti, A., Lombardot, B., Bourgine, P.: Cells tracking in a live zebrafish embryo. In: IEEE Engineering in Medicine and Biology Society, EMBS 2007 (2007)Google Scholar
  19. 19.
    Padfield, D., Rittscher, J., Roysam, B.: Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis. Medical Image Analysis 15(4) (2011)Google Scholar
  20. 20.
    Padfield, D., Rittscher, J., Thomas, N., Roysam, B.: Spatio-temporal cell cycle phase analysis using level sets and fast marching methods. Medical Image Analysis 13(1), 143–155 (2009)CrossRefGoogle Scholar
  21. 21.
    Smal, I., Meijering, E., Draegestein, K., Galjart, N., Grigoriev, I., Akhmanova, A., Van Royen, M., Houtsmuller, A., Niessen, W.: Multiple object tracking in molecular bioimaging by Rao-Blackwellized marginal particle filtering. Medical Image Analysis 12(6), 764–777 (2008)CrossRefGoogle Scholar
  22. 22.
    Sommer, C., Straehle, C., Koethe, U., Hamprecht, F.A.: ILASTIK: Interactive learning and segmentation toolkit. In: IEEE International Symposium on Biomedical Imaging, ISBI 2011 (2011)Google Scholar
  23. 23.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Computing Surveys (CSUR) 38 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bernhard X. Kausler
    • 1
  • Martin Schiegg
    • 1
  • Bjoern Andres
    • 1
    • 2
  • Martin Lindner
    • 1
  • Ullrich Koethe
    • 1
  • Heike Leitte
    • 1
  • Jochen Wittbrodt
    • 3
  • Lars Hufnagel
    • 4
  • Fred A. Hamprecht
    • 1
  1. 1.HCI/IWRHeidelberg UniversityGermany
  2. 2.SEASHarvard UniversityUnited States
  3. 3.COSHeidelberg UniversityGermany
  4. 4.European Molecular Biology Laboratory (EMBL)HeidelbergGermany

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