Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population

Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. Our proposed approach, which has the feature to ge...

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Bibliographic Details
Published in:Journal of bioscience and bioengineering 2021-02, Vol.131 (2), p.213-218
Main Authors: Tsuzuki, Yuji, Sanami, Sho, Sugimoto, Kenji, Fujita, Satoshi
Format: Article
Language:English
Subjects:
Automatic process
Automation
Cell Count - methods
Cell counting
Deep Learning
Deep neural network
Humans
Microscopy
Nuclei detection
Stain-free cells
Staining and Labeling
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Summary:Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. Our proposed approach, which has the feature to generate pseudo-nuclear stained images by simple DNN, showed remarkable advantages over existing approaches in the cell-detection and the detection of the relative position of cells for various cell densities, as well as in counting the exact cell numbers. Pseudo-nuclear staining of cells by deep learning will improve the accuracy of automated cell counting in a label-free cellular population on phase contrast images.
ISSN:1389-1723
1347-4421
DOI:10.1016/j.jbiosc.2020.09.014