Deep Learning Neural Networks Highly Predict Very Early Onset of Pluripotent Stem Cell Differentiation.
Stem Cell Reports
; 12(4): 845-859, 2019 04 09.
Article
em En
| MEDLINE
| ID: mdl-30880077
Deep learning is a significant step forward for developing autonomous tasks. One of its branches, computer vision, allows image recognition with high accuracy thanks to the use of convolutional neural networks (CNNs). Our goal was to train a CNN with transmitted light microscopy images to distinguish pluripotent stem cells from early differentiating cells. We induced differentiation of mouse embryonic stem cells to epiblast-like cells and took images at several time points from the initial stimulus. We found that the networks can be trained to recognize undifferentiated cells from differentiating cells with an accuracy higher than 99%. Successful prediction started just 20 min after the onset of differentiation. Furthermore, CNNs displayed great performance in several similar pluripotent stem cell (PSC) settings, including mesoderm differentiation in human induced PSCs. Accurate cellular morphology recognition in a simple microscopic set up may have a significant impact on how cell assays are performed in the near future.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Diferenciação Celular
/
Redes Neurais de Computação
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Células-Tronco Pluripotentes
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Aprendizado Profundo
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Stem Cell Reports
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Argentina
País de publicação:
Estados Unidos