Segmentation and Cell Tracking of Breast Cancer Cells

  • Adele P. Peskin
  • Daniel J. Hoeppner
  • Christina H. Stuelten
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)

Abstract

We describe a new technique to automatically segment and track the cell images of a breast cancer cell line in order to study cell migration and metastasis. Within each image observable cell characteristics vary widely, ranging from very bright completely bounded cells to barely visible cells with little to no apparent boundaries. A set of different segmentation algorithms are used in series to segment each cell type. Cell segmentation and cell tracking are done simultaneously, and no user selected parameters are needed. A new method for background subtraction is described and a new method of selective dilation is used to segment the barely visible cells. We show results for initial cell growth.

Keywords

Cell Boundary High Standard Deviation Bright Pixel Visible Cell 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.

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References

  1. 1.
    Chaffer, C.L., Weinberg, R.A.: A perspective on cancer cell metastasis. Science 331, 1559–1564 (2011)CrossRefGoogle Scholar
  2. 2.
    Miller, F.R., Santner, S.J., Tait, L., Dawson, P.J.: MCF10DCIS.com xenograft model of human comedo ductal carcinoma in situ. J. Natl. Cancer. Inst. 92, 1185–1186 (2000)CrossRefGoogle Scholar
  3. 3.
    Simon, I., Pound, C.R., Parin, A.W., Clemnes, J.Q., Christens-Barry, W.A.: Automated Image Analysis System for Detecting Boundaries of Live Prostate Cancer Cells. Cytometry 31, 287–294 (1998)CrossRefGoogle Scholar
  4. 4.
    Tscherepanow, M., Zollner, F., Hillebrand, M., Kummert, F.: Automatic Segmentation of Unstained Living Cells in Bright-Field Microscope Images. In: Perner, P., Salvetti, O. (eds.) MDA 2008. LNCS (LNAI), vol. 5108, pp. 158–172. Springer, Heidelberg (2008)Google Scholar
  5. 5.
    Zhang, K., Xiong, H., Yang, L., Zhou, X.: A Novel Coarse-to-Fine Adaptaton segmentation Approach for Cellular Image Analysis. In: Cham, T.-J., Cai, J., Dorai, C., Rajan, D., Chua, T.-S., Chia, L.-T. (eds.) MMM 2007. LNCS, vol. 4351, pp. 322–331. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Zanella, C., Campana, M., Rizzi, B., Melani, C., Sanguinetti, G., Bourgine, P., Mikula, K., Peyrieras, N., Sarit, A.: Cells Segmentation from 3-D Confocal Images of early Zebrafish Embryogeneis. IEEE Transactions on Image Processing (September 2009)Google Scholar
  7. 7.
    Wang, M., Zhou, X., Li, F., Huckins, J., King, R.W.: Novel cell segmentation and online SVM for cell cycle phase identification in automated microscopy. Bioinformatics 24(1), 94–101 (2007)CrossRefGoogle Scholar
  8. 8.
    Palaniappan, K., Ersoy, I., Nath, S.: Moving object segmentation using the flux tensor for biological video microscopy. In: Ip, H.H.-S., Au, O.C., Leung, H., Sun, M.-T., Ma, W.-Y., Hu, S.-M. (eds.) PCM 2007. LNCS, vol. 4810, pp. 483–493. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Stuelten, C.H., Busch, J.I., Tang, B., et al.: Transient tumor-fibroblast interactions increase tumor cell malignancy by a TGF-Beta mediated mechanism in a mouse xenograft model of breast cancer. PLoS One 5, e9832 (2010)CrossRefGoogle Scholar
  10. 10.
    Tang, B., Vu, M., Booker, T., et al.: TGF-beta switches from tumor suppressor to prometastatic factor in a model of breast cancer progression. J. Clin. Invest. 112, 1116–1124 (2003)CrossRefGoogle Scholar
  11. 11.
    Friedman, N., Russell, S.: Image segmentaton in video sequences: a probabilistic approach. In: Proceedings 13th Conference Uncertainty Artificial Intelligence (1997)Google Scholar
  12. 12.
    Kachouie, N.N., Fieguth, P., Ramunas, J., Jervis, E.: A Statistical Thresholding Method for Cell Tracking. In: IEEE International Symposium on Signal Processing and Information Technology (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Adele P. Peskin
    • 1
  • Daniel J. Hoeppner
    • 2
  • Christina H. Stuelten
    • 2
  1. 1.NISTBoulderUSA
  2. 2.NIHBethesdaUSA

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