Advertisement

A Hierarchical Bayesian Approach for Unsupervised Cell Phenotype Clustering

  • Mahesh Venkata KrishnaEmail author
  • Joachim Denzler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

We propose a hierarchical Bayesian model - the wordless Hierarchical Dirichlet Processes-Hidden Markov Model (wHDP-HMM), to tackle the problem of unsupervised cell phenotype clustering during the mitosis stages. Our model combines the unsupervised clustering capabilities of the HDP model with the temporal modeling aspect of the HMM. Furthermore, to model cell phenotypes effectively, our model uses a variant of the HDP, giving preference to morphology over co-occurrence. This is then used to model individual cell phenotype time series and cluster them according to the stage of mitosis they are in. We evaluate our method using two publicly available time-lapse microscopy video data-sets and demonstrate that the performance of our approach is generally better than the state-of-the-art.

Keywords

Hierarchical Bayesian methods Hidden Markov Models Cell phenotypes Unsupervised clustering Mitosis phase modeling Time-lapse microscopy 

Notes

Acknowledgments

The authors gratefully acknowledge financial support by ZEISS and would like to thank Christian Wojek and Stefan Saur (ZEISS Corporate Research and Technology) for helpful discussions and suggestions.

References

  1. 1.
    Beal, M.J., Ghahramani, Z., Rasmussen, C.E.: The infinite hidden markov model. In: Advances in Neural Information Processing Systems (NIPS), pp. 577–584 (2002)Google Scholar
  2. 2.
    Carpenter, A.E., Jones, T.R., Lamprecht, M.R., Clarke, C., Kang, I.H., Friman, O., Guertin, D.A., Chang, J.H., Lindquist, R.A., Moffat, J., Golland, P., Sabatini, D.M.: Cellprofiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7(10), R100 (2006)CrossRefGoogle Scholar
  3. 3.
    Gallardo, G.M., Yang, F., Ianzini, F., Mackey, M., Sonka, M.: Mitotic cell recognition with hidden markov models. In: Proceedings of SPIE, vol. 5367, 661–668 (2004)Google Scholar
  4. 4.
    Harder, N., Mora-Bermúdez, F., Godinez, W.J., Wünsche, A., Eils, R., Ellenberg, J., Rohr, K.: Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time. Genome Res. 19(11), 2113–2124 (2009)CrossRefGoogle Scholar
  5. 5.
    Held, M., Schmitz, M.H.A., Fischer, B., Walter, T., Neumann, B., Olma, M.H., Peter, M., Ellenberg, J., Gerlich, D.W.: Cellcognition: time-resolved phenotype annotation in high-throughput live cell imaging. Nat. Methods 7(9), 747–754 (2010)CrossRefGoogle Scholar
  6. 6.
    Huh, S., Chen, M.: Detection of mitosis within a stem cell population of high cell confluence in phase-contrast microscopy images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1033–1040 (2011)Google Scholar
  7. 7.
    Jiang, X., Haase, D., Körner, M., Bothe, W., Denzler, J.: Accurate 3D multi-marker tracking in X-ray cardiac sequences using a two-stage graph modeling approach. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013, Part II. LNCS, vol. 8048, pp. 117–125. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Kuettel, D., Breitenstein, M.D., Gool, L.V., Ferrari, V.: What is going on? discovering spatiotemporal dependencies in dynamic scenes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2010)Google Scholar
  9. 9.
    Liu, A.A., Li, K., Kanade, T.: Mitosis sequence detection using hidden conditional random fields. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 580–583, April 2010Google Scholar
  10. 10.
    Lodish, H., Berk, A., Kaiser, C.A., Krieger, M., Bretscher, A., Ploegh, H., Amon, A., Scott, M.P.: Molecular Cell Biology, 7th edn. W.H.Freeman & Co Ltd, New York (2013)Google Scholar
  11. 11.
    Neumann, B., Walter, T., Heriche, J.K., Bulkescher, J., Erfle, H., Conrad, C., Rogers, P., Poser, I., Held, M., Liebel, U., Cetin, C., Sieckmann, F., Pau, G., Kabbe, R., Wuensche, A., Satagopam, V., Schmitz, M.H.A., Chapuis, C., Gerlich, D.W., Schneider, R., Eils, R., Huber, W., Peters, J.M., Hyman, A.A., Durbin, R., Pepperkok, R., Ellenberg, J.: Phenotypic profiling of the human genome by time-lapse microscopy reveals cell division genes. Nature 464(7289), 721–727 (2010)CrossRefGoogle Scholar
  12. 12.
    Rematas, K., Leuven, K., Fritz, M., Tuytelaars, T.: Kernel density topic models: visual topics without visual words. In: Modern Non Parametric Methods in Machine Learning, NIPS Workshop (2012)Google Scholar
  13. 13.
    Teh, Y., Jordan, M., Beal, M., Blei, D.: Hierarchical dirichlet processes. J. Am. Stat. Assoc. 101, 1566–1581 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Wang, M., Zhou, X., Li, F., Huckins, J., King, R.W., Wong, S.T.: Novel cell segmentation and online svm for cell cycle phase identification in automated microscopy. Bioinformatics 24(1), 94–101 (2008)CrossRefGoogle Scholar
  15. 15.
    Wang, X., Ma, X., Grimson, W.: Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 539–555 (2009)CrossRefGoogle Scholar
  16. 16.
    Yang, F., Mackey, M.A., 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
  17. 17.
    Zhong, Q., Busetto, A.G., Fededa, J.P., Buhmann, J.M., Gerlich, D.W.: Unsupervised modeling of cell morphology dynamics for time-lapse microscopy. Nat. Methods 9(7), 711–713 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Computer Vision GroupFriedrich Schiller University JenaJenaGermany

Personalised recommendations