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Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields

  • Gerda Bortsova
  • Michael Sterr
  • Lichao Wang
  • Fausto Milletari
  • Nassir Navab
  • Anika Böttcher
  • Heiko Lickert
  • Fabian TheisEmail author
  • Tingying PengEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10019)

Abstract

Intestinal enteroendocrine cells secrete hormones that are vital for the regulation of glucose metabolism but their differentiation from intestinal stem cells is not fully understood. Asymmetric stem cell divisions have been linked to intestinal stem cell homeostasis and secretory fate commitment. We monitored cell divisions using 4D live cell imaging of cultured intestinal crypts to characterize division modes by means of measurable features such as orientation or shape. A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually. To assist data processing, we developed a learning based method to automatically detect mitosis events. The method contains a dual-phase framework for joint detection of dividing cells (mothers) and their progeny (daughters). In the first phase we detect mother and daughters independently using Hough Forest whilst in the second phase we associate mother and daughters by modelling their joint probability as Conditional Random Field (CRF). The method has been evaluated on 32 movies and has achieved an AUC of 72 %, which can be used in conjunction with manual correction and dramatically speed up the processing pipeline.

Keywords

Mitosis detection Hough forest Conditional random field 

References

  1. 1.
    Barker, N.: Adult intestinal stem cells: critical drivers of epithelial homeostasis and regeneration. Nat. Rev. Mol. Cell Biol. 15, 19–33 (2014)CrossRefGoogle Scholar
  2. 2.
    Snippert, H.J., et al.: Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. Cell 143, 134–144 (2010)CrossRefGoogle Scholar
  3. 3.
    Simons, B.D., Clevers, H.: Stem cell self-renewal in intestinal crypt. Exp. Cell Res. 317, 2719–2724 (2011)CrossRefGoogle Scholar
  4. 4.
    Parker, H.E., et al.: The role of gut endocrine cells in control of metabolism and appetite. Exp. Physiol. 99, 1116–1120 (2014)CrossRefGoogle Scholar
  5. 5.
    Cireşan, D.C., Giusti, A., Gambardella, L.M., Schmidhuber, J.: Mitosis detection in breast cancer histology images with deep neural networks. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 411–418. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  6. 6.
    Chen, T., Chefd’hotel, C.: Deep learning based automatic immune cell detection for immunohistochemistry images. In: Wu, G., Zhang, D., Zhou, L. (eds.) MLMI 2014. LNCS, vol. 8679, pp. 17–24. Springer, Heidelberg (2014)Google Scholar
  7. 7.
    Arteta, C., Lempitsky, V., Noble, J., Zisserman, A.: Learning to detect cells using non-overlapping extremal regions. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 348–356. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Kainz, P., Urschler, M., Schulter, S., Wohlhart, P., Lepetit, V.: You should use regression to detect cells. MICCAI 2015, Part III. LNCS, vol. 9351, pp. 276–283. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-24574-4_33 Google Scholar
  9. 9.
    Gall, J., et al.: Hough forests for object detection, tracking, and action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33, 2188–2202 (2011)CrossRefGoogle Scholar
  10. 10.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61, 55–79 (2005)CrossRefGoogle Scholar
  11. 11.
    Wang, L., et al.: Anatomic-landmark detection using graphical context modelling. In: ISBI (2015)Google Scholar
  12. 12.
    Wang, H., et al.: Landmark detection and coupled patch registration for cardiac motion tracking. In: SPIE (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Gerda Bortsova
    • 1
    • 2
  • Michael Sterr
    • 3
  • Lichao Wang
    • 1
    • 2
  • Fausto Milletari
    • 2
  • Nassir Navab
    • 2
    • 4
  • Anika Böttcher
    • 3
  • Heiko Lickert
    • 3
  • Fabian Theis
    • 1
    • 5
    Email author
  • Tingying Peng
    • 1
    • 2
    • 5
    Email author
  1. 1.Institute of Computational BiologyHelmholtz Zentrum MünchenOberschleißheimGermany
  2. 2.Computer Aided Medical ProceduresTechnische Universität MünchenMunichGermany
  3. 3.Institute of Diabetes and Regeneration ResearchHelmholtz Zentrum MünchenMunichGermany
  4. 4.Computer Aided Medical ProceduresJohns Hopkins UniversityBaltimoreUSA
  5. 5.Chair of Mathematical Modelling of Bioloigcal SystemsTechnische Universität MünchenMunichGermany

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