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Open AccessArticle

Deep Learning for Non-Invasive Determination of the Differentiation Status of Human Neuronal Cells by Using Phase-Contrast Photomicrographs

1
Department of Biosciences and Informatics, Keio University, Kanagawa 223-8522, Japan
2
Faculty of Pharmaceutical Sciences, Sanyo-Onoda City University, Yamaguchi 756-0884, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(24), 5503; https://doi.org/10.3390/app9245503
Received: 26 November 2019 / Revised: 10 December 2019 / Accepted: 11 December 2019 / Published: 14 December 2019
(This article belongs to the Special Issue Image Processing Techniques for Biomedical Applications)
Regenerative medicine using neural stem cells (NSCs), which self-renew and have pluripotency, has recently attracted a lot of interest. Much research has focused on the transplantation of differentiated NSCs to damaged tissues for the treatment of various neurodegenerative diseases and spinal cord injuries. However, current approaches for distinguishing differentiated from non-differentiated NSCs at the single-cell level have low reproducibility or are invasive to the cells. Here, we developed a fully automated, non-invasive convolutional neural network-based model to determine the differentiation status of human NSCs at the single-cell level from phase-contrast photomicrographs; after training, our model showed an accuracy of identification greater than 94%. To understand how our model distinguished between differentiated and non-differentiated NSCs, we evaluated the informative features it learned for the two cell types and found that it had learned several biologically relevant features related to NSC shape during differentiation. We also used our model to examine the differentiation of NSCs over time; the findings confirmed our model’s ability to distinguish between non-differentiated and differentiated NSCs. Thus, our model was able to non-invasively and quantitatively identify differentiated NSCs with high accuracy and reproducibility, and, therefore, could be an ideal means of identifying differentiated NSCs in the clinic. View Full-Text
Keywords: convolutional neural networks; deep learning; image-wise classification; SH-SY5Y cells convolutional neural networks; deep learning; image-wise classification; SH-SY5Y cells
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Ooka, M.; Tokuoka, Y.; Nishimoto, S.; Hiroi, N.F.; Yamada, T.G.; Funahashi, A. Deep Learning for Non-Invasive Determination of the Differentiation Status of Human Neuronal Cells by Using Phase-Contrast Photomicrographs. Appl. Sci. 2019, 9, 5503.

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