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Segmentation and Classification of Cell Cycle Phases in Fluorescence Imaging

  • Ilker Ersoy
  • Filiz Bunyak
  • Vadim Chagin
  • M. Christina Cardoso
  • Kannappan Palaniappan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

Current chemical biology methods for studying spatiotemporal correlation between biochemical networks and cell cycle phase progression in live-cells typically use fluorescence-based imaging of fusion proteins. Stable cell lines expressing fluorescently tagged protein GFP-PCNA produce rich, dynamically varying sub-cellular foci patterns characterizing the cell cycle phases, including the progress during the S-phase. Variable fluorescence patterns, drastic changes in SNR, shape and position changes and abundance of touching cells require sophisticated algorithms for reliable automatic segmentation and cell cycle classification. We extend the recently proposed graph partitioning active contours (GPAC) for fluorescence-based nucleus segmentation using regional density functions and dramatically improve its efficiency, making it scalable for high content microscopy imaging. We utilize surface shape properties of GFP-PCNA intensity field to obtain descriptors of foci patterns and perform automated cell cycle phase classification, and give quantitative performance by comparing our results to manually labeled data.

Keywords

Support Vector Machine Proliferate Cell Nuclear Antigen Active Contour Cell Cycle Phase Cell Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Easwaran, H., Leonhardt, H., Cardoso, M.: Cell cycle markers for live cell analyses. Cell Cycle 4(3), 453–455 (2005)Google Scholar
  2. 2.
    Sporbert, A., Gahl, A., Ankerhold, R., Leonhardt, H., Cardoso, M.: DNA polymerase clamp shows little turnover at established replication sites but sequential de novo assembly at adjacent origin clusters. Molecular Cell 10(6), 1355–1365 (2002)CrossRefGoogle Scholar
  3. 3.
    Leonhardt, H., Rahn, H.-P., Weinzierl, P., Sporbert, A., Cremer, T., Zink, D., Cardoso, M.: Dynamics of DNA replication factories in living cells. J. Cell Biology 149(2), 271–280 (2000)CrossRefGoogle Scholar
  4. 4.
    Bunyak, F., Palaniappan, K., Nath, S., Baskin, T., Dong, G.: Quantitative cell motility for in vitro wound healing using level set-based active contour tracking. In: Proc. IEEE Int. Symp. Biomedical Imaging, April 2006, pp. 1040–1043 (2006)Google Scholar
  5. 5.
    Nath, S.K., Palaniappan, K., Bunyak, F.: Cell segmentation using coupled level sets and graph-vertex coloring. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4190, pp. 101–108. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Ersoy, I., Bunyak, F., Palaniappan, K., Sun, M., Forgacs, G.: Cell spreading analysis with directed edge profile-guided level set active contours. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 376–383. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    Sumengen, B., Manjunath, B.: Graph partitioning active contours (GPAC) for image segmentation. IEEE Trans. Patt. Anal. Mach. Intell., 509–521 (April 2006)Google Scholar
  10. 10.
    Bertelli, L., Sumengen, B., Manjunath, B., Gibou, F.: A variational framework for multi-region pairwise similarity-based image segmentation. IEEE Trans. Patt. Anal. Mach. Intell., 1400–1414 (August 2008)Google Scholar
  11. 11.
    Sethian, J.: Level set methods and fast marching methods, 2nd edn. Cambridge Univ. Press, Cambridge (1999)zbMATHGoogle Scholar
  12. 12.
    Vese, L., Chan, T.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Computer Vision 50(3), 271–293 (2002)zbMATHCrossRefGoogle Scholar
  13. 13.
    Boland, M.V., Murphy, R.F.: A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics 17(12), 1213–1223 (2001)CrossRefGoogle Scholar
  14. 14.
    Shamir, L., Orlov, N., Eckley, D.M., Macura, T., Johnston, J., Goldberg, I.: Wndchrm - An open source utility for biological image analysis. Source Code for Biology and Medicine 3(1) (2008)Google Scholar
  15. 15.
    Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ilker Ersoy
    • 1
  • Filiz Bunyak
    • 1
  • Vadim Chagin
    • 2
    • 3
  • M. Christina Cardoso
    • 2
  • Kannappan Palaniappan
    • 1
  1. 1.Department of Computer ScienceUniversity of Missouri ColumbiaUSA
  2. 2.Department of BiologyTechnische Universität DarmstadtGermany
  3. 3.Institute of CytologyRussian Academy of SciencesSt. PetersburgRussia

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