사람의 다분화가능 잠재세포 콜로니를, 이미지 분석을 통해 판별

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

full text PDF(+)

 

중요도(임상적파급력, 유용성) :2

회기적신기술사용수준 : 1.5

날짜 : 2016-07
저널 : CMMM 의공학저널

국가 : 핀란드
대학 또는 연구소 :  탬페레(Tampere) 의대,공대

저자:

한줄요약 : human induced pluripotent stem cells (hiPSCs)의 colony가 잘 자라는지 감시하는 건 사람의 수작업으로 해왔는데 이것을 SVM 을 이용해서, 잘 자라는 good colony인지 bad colony 인지 판별을 자동으로 하게 했다. Colony-growing 에 대한 quality control 은 결국 image analysis 이다.

 

Link

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4963598/

https://www.hindawi.com/journals/cmmm/2016/3091039/

Joutsijoki-2016-Machine Learning Approach to A.pdf

Keywords

 

 

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Comput Math Methods Med. 2016;2016:3091039. doi: 10.1155/2016/3091039. Epub 2016 Jul 14. Machine Learning Approach to Automated Quality Identification of Human Induced Pluripotent Stem Cell Colony Images. Joutsijoki H1, Haponen M2, Rasku J1, Aalto-Setälä K3, Juhola M1. Author information: 1 School of Information Sciences, University of Tampere, Kanslerinrinne 1, FI-33014 Tampere, Finland. 2 BioMediTech, University of Tampere, Biokatu 12, FI-33520 Tampere, Finland. 3 School of Medicine, University of Tampere, Biokatu 12, FI-33520 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. PMCID: PMC4963598 Free PMC Article

 

 

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