Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images
Joutsijoki, Henry; Haponen, Markus; Rasku, Jyrki; Aalto-Setälä, Katriina; Juhola, Martti (2016)
Computational and Mathematical Methods in Medicine 2016 2016
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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. (CC BY 4.0)
Thefocus 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 bywhich 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 learningmethods such asmulticlass Support VectorMachines and several baseline methods together with Scaled Invariant FeatureTransformation based features.We performover 80 test arrangements and do a thorough parameter value search.Thebest accuracy (62.4%) for classification was obtained by using a 𝑘-NN classifier showing improved accuracy compared to earlier studies.
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