Construction of a system using a deep learning algorithm to count cell numbers in nanoliter wells for viable single-cell experiments

For single-cell experiments, it is important to accurately count the number of viable cells in a nanoliter well. We used a deep learning-based convolutional neural network (CNN) on a large amount of digital data obtained as microscopic images. The training set consisted of 103 019 samples, each repr...

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Detalles Bibliográficos
Autores principales: Kamatani, Takashi, Fukunaga, Koichi, Miyata, Kaede, Shirasaki, Yoshitaka, Tanaka, Junji, Baba, Rie, Matsusaka, Masako, Kamatani, Naoyuki, Moro, Kazuyo, Betsuyaku, Tomoko, Uemura, Sotaro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715092/
https://www.ncbi.nlm.nih.gov/pubmed/29203784
http://dx.doi.org/10.1038/s41598-017-17012-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5715092/
https://www.ncbi.nlm.nih.gov/pubmed/29203784
http://dx.doi.org/10.1038/s41598-017-17012-x

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