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

Research output: Chapter in Book/Report/Conference proceedingsConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationComputer Aided Systems Theory – EUROCAST 2015 - 15th International Conference, Revised Selected Papers
EditorsFranz Pichler, Roberto Moreno-Díaz, Alexis Quesada-Arencibia
PublisherSpringer Verlag
Pages318-325
Number of pages8
ISBN (Print)9783319273396
DOIs
Publication statusPublished - 2015
Event15th International Conference on Computer Aided Systems Theory, Eurocast 2015 - Las Palmas, Gran Canaria, Spain
Duration: 8 Feb 201513 Feb 2015
http://eurocast2015.fulp.ulpgc.es/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9520
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Computer Aided Systems Theory, Eurocast 2015
CountrySpain
CityLas Palmas, Gran Canaria
Period08.02.201513.02.2015
Internet address

Keywords

  • Hybride machine learning ensembles
  • Image processing
  • Osteogenic tissue engineering

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