Author/Authors :
Joutsijoki, Henry School of Information Sciences - University of Tampere - Kanslerinrinne - Tampere, Finland , Haponen, Markus University of Tampere - Biokatu - Tampere, Finland , Rasku, Jyrki School of Information Sciences - University of Tampere - Kanslerinrinne - Tampere, Finland , Aalto-Setälä, Katriina School of Medicine - University of Tampere - Biokatu - Tampere, Finland , Juhola, Martti School of Information Sciences - University of Tampere - Kanslerinrinne - Tampere, Finland
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 𝑘-NN classifier showing improved accuracy compared to earlier studies.