Authors:
Ryouichi Furukawa
1
;
Yohei Hayashi
2
;
Hideaki Kano
3
;
Junichi Matsumoto
4
;
Shoichi Honda
4
and
Kazuhiro Hotta
1
Affiliations:
1
Meijo University, Nagoya-shi, Japan
;
2
RIKEN, Tsukuba-shi, Japan
;
3
Department of Chemistry, Faculty of Science, Kyushu University, Fukuoka-shi, Japan
;
4
KATAOKA CORPORATION, Kyoto-shi, Japan
Keyword(s):
CARS Microscope, iPS Cells, Effective Spectrum, Automatic Discovery.
Abstract:
There is a technique using the CARS (Coherent Anti-Stokes Raman Scattering) microscope to identify iPS cells. CARS microscope can visualize the different molecular structures of iPS cells in each spectrum, so it is possible to identify iPS cells without destroying them. However, the information on molecules in the spectrum obtained by the CARS microscope is so diverse that it takes a great deal of time and effort to identify them. We propose a method to automatically identify the spectrum, which is effective for iPS cell identification, thereby reducing the time and effort required for identification using the CARS microscope. In this paper, we propose a network that handles multi-resolution information in parallel to learn both image classification and segmentation simultaneously. Moreover, the effective spectrum for classifying iPS cells are discovered by using the network gradients and the F-measure for cell segmentation. By the experiments on four kinds of iPS cells, we confirmed
that the accuracy of the proposed method for classifying iPS cells achieved 99%. Furthermore, the effective spectrum for each iPS cell could be automatically identified.
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