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Construction of a system using a deep learning algorithm to count cell numbers in nanoliter wells for viable single-cell experiments.
Kamatani, Takashi; Fukunaga, Koichi; Miyata, Kaede; Shirasaki, Yoshitaka; Tanaka, Junji; Baba, Rie; Matsusaka, Masako; Kamatani, Naoyuki; Moro, Kazuyo; Betsuyaku, Tomoko; Uemura, Sotaro.
Affiliation
  • Kamatani T; Pulmonary Division, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan.
  • Fukunaga K; Pulmonary Division, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan. km-fuku@cpnet.med.keio.ac.jp.
  • Miyata K; Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
  • Shirasaki Y; Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
  • Tanaka J; StaGen Co. Ltd, Taito-ku, Tokyo, Japan.
  • Baba R; Pulmonary Division, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan.
  • Matsusaka M; Pulmonary Division, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan.
  • Kamatani N; StaGen Co. Ltd, Taito-ku, Tokyo, Japan.
  • Moro K; RIKEN Center for Integrative Medical Sciences, Tsurumi-ku, Yokohama, Kanagawa, Japan.
  • Betsuyaku T; Pulmonary Division, Department of Medicine, Keio University School of Medicine, 35 Shinanomachi Shinjuku-ku, Tokyo 160-8582, Japan.
  • Uemura S; Department of Biological Sciences, Graduate School of Science, The University of Tokyo, Bunkyo-ku, Tokyo, Japan.
Sci Rep ; 7(1): 16831, 2017 12 04.
Article in En | MEDLINE | ID: mdl-29203784
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Cell Count / Deep Learning Type of study: Prognostic_studies Limits: Humans Language: En Journal: Sci Rep Year: 2017 Type: Article Affiliation country: Japan
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Cell Count / Deep Learning Type of study: Prognostic_studies Limits: Humans Language: En Journal: Sci Rep Year: 2017 Type: Article Affiliation country: Japan