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Your search keyword '"Amane Hirotsu"' showing total 2 results
Start Over Author "Amane Hirotsu" Publisher public library of science (plos)
2 results on '"Amane Hirotsu"'

1. Label-free classification of cells based on supervised machine learning of subcellular structures.

Author

Yusuke Ozaki, Hidenao Yamada, Hirotoshi Kikuchi, Amane Hirotsu, Tomohiro Murakami, Tomohiro Matsumoto, Toshiki Kawabata, Yoshihiro Hiramatsu, Kinji Kamiya, Toyohiko Yamauchi, Kentaro Goto, Yukio Ueda, Shigetoshi Okazaki, Masatoshi Kitagawa, Hiroya Takeuchi, and Hiroyuki Konno

Subjects
Medicine, Science
Abstract

It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.

Published
2019
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2. Label-free classification of cells based on supervised machine learning of subcellular structures

Author

Masatoshi Kitagawa, Yusuke Ozaki, Amane Hirotsu, Toshiki Kawabata, Tomohiro Matsumoto, Kinji Kamiya, Hiroyuki Konno, Hirotoshi Kikuchi, Tomohiro Murakami, Shigetoshi Okazaki, Yukio Ueda, Toyohiko Yamauchi, Hidenao Yamada, Yoshihiro Hiramatsu, Kentaro Goto, and Hiroya Takeuchi

Subjects
Computer science, Optical Analysis, computer.software_genre, Immune Receptors, Biochemistry, 01 natural sciences, Pattern Recognition, Automated, Bright Field Microscopy, Machine Learning, Leukocytes, Medicine and Health Sciences, Staining, Microscopy, 0303 health sciences, Immune System Proteins, Multidisciplinary, Refractive Index, Light Microscopy, Cell Staining, Hep G2 Cells, Cell lines, Medicine, Supervised Machine Learning, Single-Cell Analysis, Biological cultures, Research Article, Signal Transduction, Computer and Information Sciences, Imaging Techniques, Science, Immunology, Research and Analysis Methods, Machine learning, Cell Line, 010309 optics, 03 medical and health sciences, Text mining, Artificial Intelligence, Support Vector Machines, 0103 physical sciences, Humans, Chemical Characterization, 030304 developmental biology, Label free, business.industry, Biology and Life Sciences, Proteins, Cell Biology, HCT116 Cells, Support vector machine, SW480 cells, Specimen Preparation and Treatment, Artificial intelligence, Pattern Recognition Receptors, business, Classifier (UML), computer
Abstract

It is demonstrated that cells can be classified by pattern recognition of the subcellular structure of non-stained live cells, and the pattern recognition was performed by machine learning. Human white blood cells and five types of cancer cell lines were imaged by quantitative phase microscopy, which provides morphological information without staining quantitatively in terms of optical thickness of cells. Subcellular features were then extracted from the obtained images as training data sets for the machine learning. The built classifier successfully classified WBCs from cell lines (area under ROC curve = 0.996). This label-free, non-cytotoxic cell classification based on the subcellular structure of QPM images has the potential to serve as an automated diagnosis of single cells.

Published
2019

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