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Paper Abstract and Keywords
Presentation 2017-01-18 14:15
A Machine Learning Algorithm for the Automatic and Non-invasive Quality Assessment of Confluent Cells
Kazuki Sato (Yamagata Univ.), Hiroto Sasaki, Ryuji Kato (Nagoya Univ.), Tetsuya Yuasa, Siu Kang (Yamagata Univ.) MI2016-98
Abstract (in Japanese) (See Japanese page) 
(in English) In the research field of regenerative medicine, non-invasive method of cell quality classification has been expected for safety clinical application. We have implemented the automatic image-based algorithm for the normal human dermal fibroblasts (NDHF) cells. The quality evaluation of confluent NHDFs has potential difficulty because they are highly dense and their orientations shows diversity. To quantified their orientation heterogeneity, we applied scale-invariance feature transform (SIFT) onto the image obtained through some image-processings such as noise-elimination, morphological filtering, skeltonization, and so on. Furthermore, we performed the kernel support vector machine for the feature values to classify the remaining lifespan of cells and condition of culturing. As a result, we provide preliminary algorithms for automatic and non-invasive assessment of cell quality to promote clinical application on tissue engineering.
Keyword (in Japanese) (See Japanese page) 
(in English) Regenerative medicine / Phase-contrast microscope / Normal human dermal fibroblasts (NHDF) / Cellular image / Scale-Invariant Feature Transform (SIFT) / Support Vector Machine (SVM) / Pattern recognition / Convolution neural network (CNN)  
Reference Info. IEICE Tech. Rep., vol. 116, no. 393, MI2016-98, pp. 101-106, Jan. 2017.
Paper # MI2016-98 
Date of Issue 2017-01-11 (MI) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee MI  
Conference Date 2017-01-18 - 2017-01-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Tenbusu Naha 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Image Engineering, Analysis, Recognition, etc. 
Paper Information
Registration To MI 
Conference Code 2017-01-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Machine Learning Algorithm for the Automatic and Non-invasive Quality Assessment of Confluent Cells 
Sub Title (in English)  
Keyword(1) Regenerative medicine  
Keyword(2) Phase-contrast microscope  
Keyword(3) Normal human dermal fibroblasts (NHDF)  
Keyword(4) Cellular image  
Keyword(5) Scale-Invariant Feature Transform (SIFT)  
Keyword(6) Support Vector Machine (SVM)  
Keyword(7) Pattern recognition  
Keyword(8) Convolution neural network (CNN)  
1st Author's Name Kazuki Sato  
1st Author's Affiliation Yamagata University (Yamagata Univ.)
2nd Author's Name Hiroto Sasaki  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Ryuji Kato  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Tetsuya Yuasa  
4th Author's Affiliation Yamagata University (Yamagata Univ.)
5th Author's Name Siu Kang  
5th Author's Affiliation Yamagata University (Yamagata Univ.)
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Speaker Author-1 
Date Time 2017-01-18 14:15:00 
Presentation Time 40 minutes 
Registration for MI 
Paper # MI2016-98 
Volume (vol) vol.116 
Number (no) no.393 
Page pp.101-106 
#Pages 6 
Date of Issue 2017-01-11 (MI) 

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