Advertisement

Prediction of Stem Cell Differentiation in Human Amniotic Membrane Images Using Machine Learning

  • Lisa Obritzberger
  • Daniela Borgmann
  • Susanne Schaller
  • Viktoria Dorfer
  • Andrea Lindenmair
  • Susanne Wolbank
  • Simone Hennerbichler
  • Heinz Redl
  • Stephan Winkler
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9520)

Abstract

It has been shown that it is possible to differentiate viable amniotic membrane towards osteogenic lineage, i.e. bony tissue. This process of mineralization may take several weeks and can show different manifestations per sample. The tissue can only be used, when the mineralization process is advanced in a certain degree. Therefore, a forecast of the development of mineralization would be helpful to save time and resources. This paper shows how a prediction on the development of mineralization can be made by using several image processing techniques, machine learning methods, and hybrid ensembles of machine learning algorithms.

Keywords

Osteogenic tissue engineering Hybride machine learning ensembles Image processing 

References

  1. 1.
    Faulk, W., Matthews, R., Stevens, P., Bennett, J., Burgos, H., Hsi, B.: Human amnion as an adjunct in wound healing. Lancet 1, 1156–1158 (1980)CrossRefGoogle Scholar
  2. 2.
    Gruss, J., Jirsch, D.: Human amniotic membrane: a versatile wound dressing. Can. Med. Assoc. J. 118, 1237–1246 (1978)Google Scholar
  3. 3.
    Ganatra, M.: Amniotic membrane in surgery. J. Pak. Med. Assoc. 53, 29–32 (2003)Google Scholar
  4. 4.
    Lindenmair, A., Wolbank, S., Stadler, G., Meinl, A., Peterbauer-Scherb, A., Eibl, J., Polin, H., Gabriel, C., van Griensven, M., Redl, H.: Osteogenic differentiation of intact human amniotic membrane. Biomaterials 31, 8659–8665 (2010)CrossRefGoogle Scholar
  5. 5.
    Burger, W., Burge, M.: Principles of Digital Image Processing: Fundamental Techniques. Springer, London (2011)zbMATHGoogle Scholar
  6. 6.
    Ljung, L.: System Identification - Theory For the User, 2nd edn. PTR Prentice-Hall, Upper Saddle River, NJ (1999)zbMATHGoogle Scholar
  7. 7.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  8. 8.
    Breiman, L.: Random forests. Mach. Learn. 45, 5–32 (2001)CrossRefzbMATHGoogle Scholar
  9. 9.
    Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  10. 10.
    Haykin, S.: Neural Networks. A Comprehensive Foundation. PTR Prentice-Hall, Upper Saddle River, NJ (1999)zbMATHGoogle Scholar
  11. 11.
    Wagner, S., Kronberger, G., Beham, A., Kommenda, M., Scheibenpflug, A., Pitzer, E., Vonolfen, S., Kofler, M., Winkler, S., Dorfer, V., Affenzeller, M.: Architecture and design of the HeuristicLab optimization environment. In: Klempous, R., Nikodem, J., Jacak, W., Chaczko, Z. (eds.) Advanced Methods and Applications in Computational Intelligence. TIEI, vol. 6, pp. 193–258. Springer, Heidelberg (2013) Google Scholar
  12. 12.
    Winkler, S., Schaller, S., Dorfer, V., Affenzeller, M., Petz, G., Karpowicz, M.: Data-based prediction of sentiments using heterogeneous model ensembles. Soft Comput. 18, 1–12 (2014)Google Scholar
  13. 13.
    Obritzberger, L., Borgmann, D., Schaller, S., Dorfer, V., Lindenmair, A., Wolbank, S., Redl, H., Winkler, S.: Prediction of mineralization degree in human amniotic membrane using image processing techniques and machine learning. To be published in Proceedings of the XI Metaheuristics International Conference (MIC2015) (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Lisa Obritzberger
    • 1
  • Daniela Borgmann
    • 1
  • Susanne Schaller
    • 1
  • Viktoria Dorfer
    • 1
  • Andrea Lindenmair
    • 2
    • 3
  • Susanne Wolbank
    • 2
    • 3
  • Simone Hennerbichler
    • 4
  • Heinz Redl
    • 2
    • 3
  • Stephan Winkler
    • 1
  1. 1.Bioinformatics Research GroupUniversity of Applied Sciences Upper AustriaHagenbergAustria
  2. 2.Ludwig Boltzmann Institute for Experimental and Clinical TraumatologyAUVA Research CenterViennaAustria
  3. 3.Trauma Care ConsultTraumatologische Forschung Gemeinnützige GmbHViennaAustria
  4. 4.Red Cross Blood Transfusion Service for Upper AustriaAustrian Cluster for Tissue RegenerationLinzAustria

Personalised recommendations