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PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning

Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained....

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Autores principales: Rivenson, Yair, Liu, Tairan, Wei, Zhensong, Zhang, Yibo, de Haan, Kevin, Ozcan, Aydogan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6363787/
https://www.ncbi.nlm.nih.gov/pubmed/30728961
http://dx.doi.org/10.1038/s41377-019-0129-y
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author Rivenson, Yair
Liu, Tairan
Wei, Zhensong
Zhang, Yibo
de Haan, Kevin
Ozcan, Aydogan
author_facet Rivenson, Yair
Liu, Tairan
Wei, Zhensong
Zhang, Yibo
de Haan, Kevin
Ozcan, Aydogan
author_sort Rivenson, Yair
collection PubMed
description Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones’ stain, and Masson’s trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning.
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spelling pubmed-63637872019-02-06 PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning Rivenson, Yair Liu, Tairan Wei, Zhensong Zhang, Yibo de Haan, Kevin Ozcan, Aydogan Light Sci Appl Article Using a deep neural network, we demonstrate a digital staining technique, which we term PhaseStain, to transform the quantitative phase images (QPI) of label-free tissue sections into images that are equivalent to the brightfield microscopy images of the same samples that are histologically stained. Through pairs of image data (QPI and the corresponding brightfield images, acquired after staining), we train a generative adversarial network and demonstrate the effectiveness of this virtual-staining approach using sections of human skin, kidney, and liver tissue, matching the brightfield microscopy images of the same samples stained with Hematoxylin and Eosin, Jones’ stain, and Masson’s trichrome stain, respectively. This digital-staining framework may further strengthen various uses of label-free QPI techniques in pathology applications and biomedical research in general, by eliminating the need for histological staining, reducing sample preparation related costs and saving time. Our results provide a powerful example of some of the unique opportunities created by data-driven image transformations enabled by deep learning. Nature Publishing Group UK 2019-02-06 /pmc/articles/PMC6363787/ /pubmed/30728961 http://dx.doi.org/10.1038/s41377-019-0129-y Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Rivenson, Yair
Liu, Tairan
Wei, Zhensong
Zhang, Yibo
de Haan, Kevin
Ozcan, Aydogan
PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
title PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
title_full PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
title_fullStr PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
title_full_unstemmed PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
title_short PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning
title_sort phasestain: the digital staining of label-free quantitative phase microscopy images using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6363787/
https://www.ncbi.nlm.nih.gov/pubmed/30728961
http://dx.doi.org/10.1038/s41377-019-0129-y
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