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Computational and Mathematical Methods in Medicine
Volume 2016, Article ID 3091039, 15 pages
http://dx.doi.org/10.1155/2016/3091039
Research Article

Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images

1School of Information Sciences, University of Tampere, Kanslerinrinne 1, FI-33014 Tampere, Finland
2BioMediTech, University of Tampere, Biokatu 12, FI-33520 Tampere, Finland
3School of Medicine, University of Tampere, Biokatu 12, FI-33520 Tampere, Finland

Received 2 February 2016; Revised 16 May 2016; Accepted 2 June 2016

Academic Editor: Issam El Naqa

Copyright © 2016 Henry Joutsijoki et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The focus of this research is on automated identification of the quality of human induced pluripotent stem cell (iPSC) colony images. iPS cell technology is a contemporary method by which the patient’s cells are reprogrammed back to stem cells and are differentiated to any cell type wanted. iPS cell technology will be used in future to patient specific drug screening, disease modeling, and tissue repairing, for instance. However, there are technical challenges before iPS cell technology can be used in practice and one of them is quality control of growing iPSC colonies which is currently done manually but is unfeasible solution in large-scale cultures. The monitoring problem returns to image analysis and classification problem. In this paper, we tackle this problem using machine learning methods such as multiclass Support Vector Machines and several baseline methods together with Scaled Invariant Feature Transformation based features. We perform over 80 test arrangements and do a thorough parameter value search. The best accuracy (62.4%) for classification was obtained by using a -NN classifier showing improved accuracy compared to earlier studies.