Seeded Watersheds for Combined Segmentation and Tracking of Cells

  • Amalka Pinidiyaarachchi
  • Carolina Wählby
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds from the current frame, and over-segmentation is reduced by rule-based merging, propagating labels from one time-frame to the next. Thus, watershed segmentation is used for segmentation as well as tracking of cells over time. The described algorithm was tested on neural stem/progenitor cells imaged using time-lapse microscopy. Tracking results agreed to 71% to manual tracking results. The results were also compared to tracking based on solving the assignment problem using a modified version of the auction algorithm.

Keywords

Catchment Basin Watershed Segmentation Merging Step Auction Algorithm Background Seed 
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.
    Leymarie, F., Levine, M.: Tracking deformable objects in the plane using an active contour model. IEEE Trans. Pattern Anal. Machine Intell 15(6), 617–634 (1993)CrossRefGoogle Scholar
  2. 2.
    Kirubarajan, T., Bar-Shalom, Y., Pattipati, K.: Multiassignment for tracking a large number of overlapping objects. IEEE Trans. Aerosp. Electron. Syst. 37(1), 2–21 (2001)Google Scholar
  3. 3.
    Umesh Adiga, P., Chaudhuri, S.: Segmentation of volumetric histopathological images by surface following using constrained snakes. In: Proc. of 14th Int. Conf. Pattern Recogn., vol. 2, pp. 1674–1676 (1998)Google Scholar
  4. 4.
    Althoff, K., Wählby, C., Faijerson, J., Degerman, J., Pinidiyaarachchi, A., Gedda, M., Karlsson, P., Olsson, T., Eriksson, P., Bengtsson, E., Thorlin, T., Gustavsson, T.: Time-lapse microscopy and image analysis for in vitro cell migration analysis (submitted)Google Scholar
  5. 5.
    Sahoo, P., Soltani, S., Wong, A., Chen, Y.: A survey of thresholding techniques. Comp. Vis. Graph. Im. Proc., vol. 41, pp. 233–260 (1988)Google Scholar
  6. 6.
    Gustavsson, T., Althoff, K., Degerman, J., Olsson, T., Thoreson, A.-C., Thorlin, T., Eriksson, P.: Time-lapse microscopy and image processing for stem cell research modeling cell migration. In: Medical Imaging 2003: Image Processing, vol. 5032, pp. 1–15 (2003)Google Scholar
  7. 7.
    Bertsekas, D.: Auction algorithms, in Linear Network Optimization: Algorithms and Codes, 1st edn., pp. 167–244. The MIT Press, Cambridge (1991)Google Scholar
  8. 8.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Heidelberg (1999)MATHGoogle Scholar
  9. 9.
    Vincent, L., Soille, P.: Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Pattern Anal. Machine Intell. 13(6), 583–598 (1991)CrossRefGoogle Scholar
  10. 10.
    Wu, K., Gauthier, D., Levine, M.: Live cell image segmentation. IEEE Trans. Biomed. Eng. 42(1), 1–12 (1995)CrossRefGoogle Scholar
  11. 11.
    Ortiz de Solorzano, C., Garcia Rodriguez, E., Jones, A., Pinkel, D., Gray, J., Sudar, D., Lockett, S.: Segmentation of confocal microscope images of cell nuclei in thick tissue sections. Journal of Microscopy 193, 212–226 (1999)CrossRefGoogle Scholar
  12. 12.
    Beucher, S., Lantuéjoul, C.: Use of watersheds in contour detection. In: Int. Workshop on Image Processing, CCETT, Rennes, France (1979)Google Scholar
  13. 13.
    Vincent, L.: Morphological grayscale reconstruction in image analysis: applications and efficient algorithms. IEEE Trans. Image Processing 2(2), 176–201 (1993)CrossRefGoogle Scholar
  14. 14.
    Landini, G., Othman, E.: Estimation of tissue layer level by sequential morphological reconstruction. Journal of Microscopy 209(2), 118–125 (2003)CrossRefMathSciNetGoogle Scholar
  15. 15.
    Wählby, C., Sintorn, I.-M., Erlandsson, F., Borgefors, G., Bengtsson, E.: Combining intensity, edge, and shape information for 2D and 3D segmentation of cell nuclei in tissue sections. Journal of Microscopy 215(1), 67–76 (2004)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Zimmer, C., Labruyere, E., Meas-Yedid, V., Guillen, N., Olivo- Marin, J.-C.: Segmentation and tracking of migrating cells in videomicroscopy with parametric active contours: a tool for cell-based drug testing. IEEE Trans. Med. Imag. 21(10), 1212–1221 (2002)CrossRefGoogle Scholar
  17. 17.
    Debeir, O., Camby, I., Kiss, R., Van Ham, P., Decaestecker, C.: A model-based approach for automated in vitro cell tracking and chemotaxis analyses. Cytometry 60A, 29–40 (2004)CrossRefGoogle Scholar
  18. 18.
    Demou, Z., McIntire, L.: Fully automated three-dimensional tracking of cancer cells in collagen gels: Determination of motility phenotypes at the cellular level. Cancer Research 62, 5301–5307 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Amalka Pinidiyaarachchi
    • 1
    • 2
  • Carolina Wählby
    • 1
  1. 1.Centre for Image AnalysisUppsala UniversitySweden
  2. 2.Dept. of Statistics and Computer ScienceUniversity of PeradeniyaSri Lanka

Personalised recommendations