Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.

Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as tho...

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Autores principales:	Håkan Wieslander, Ankit Gupta, Ebba Bergman, Erik Hallström, Philip John Harrison
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f5a33d28812c4130a3416306c4697143
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spelling	oai:doaj.org-article:f5a33d28812c4130a3416306c46971432021-12-02T20:13:39ZLearning to see colours: Biologically relevant virtual staining for adipocyte cell images.1932-620310.1371/journal.pone.0258546https://doaj.org/article/f5a33d28812c4130a3416306c46971432021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0258546https://doaj.org/toc/1932-6203Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images using virtual staining (also known as "label-free prediction" and "in-silico labeling") can get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key features required for downstream analysis of these channels.Håkan WieslanderAnkit GuptaEbba BergmanErik HallströmPhilip John HarrisonPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10, p e0258546 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Håkan Wieslander Ankit Gupta Ebba Bergman Erik Hallström Philip John Harrison Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.
description	Fluorescence microscopy, which visualizes cellular components with fluorescent stains, is an invaluable method in image cytometry. From these images various cellular features can be extracted. Together these features form phenotypes that can be used to determine effective drug therapies, such as those based on nanomedicines. Unfortunately, fluorescence microscopy is time-consuming, expensive, labour intensive, and toxic to the cells. Bright-field images lack these downsides but also lack the clear contrast of the cellular components and hence are difficult to use for downstream analysis. Generating the fluorescence images directly from bright-field images using virtual staining (also known as "label-free prediction" and "in-silico labeling") can get the best of both worlds, but can be very challenging to do for poorly visible cellular structures in the bright-field images. To tackle this problem deep learning models were explored to learn the mapping between bright-field and fluorescence images for adipocyte cell images. The models were tailored for each imaging channel, paying particular attention to the various challenges in each case, and those with the highest fidelity in extracted cell-level features were selected. The solutions included utilizing privileged information for the nuclear channel, and using image gradient information and adversarial training for the lipids channel. The former resulted in better morphological and count features and the latter resulted in more faithfully captured defects in the lipids, which are key features required for downstream analysis of these channels.
format	article
author	Håkan Wieslander Ankit Gupta Ebba Bergman Erik Hallström Philip John Harrison
author_facet	Håkan Wieslander Ankit Gupta Ebba Bergman Erik Hallström Philip John Harrison
author_sort	Håkan Wieslander
title	Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.
title_short	Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.
title_full	Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.
title_fullStr	Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.
title_full_unstemmed	Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.
title_sort	learning to see colours: biologically relevant virtual staining for adipocyte cell images.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/f5a33d28812c4130a3416306c4697143
work_keys_str_mv	AT hakanwieslander learningtoseecoloursbiologicallyrelevantvirtualstainingforadipocytecellimages AT ankitgupta learningtoseecoloursbiologicallyrelevantvirtualstainingforadipocytecellimages AT ebbabergman learningtoseecoloursbiologicallyrelevantvirtualstainingforadipocytecellimages AT erikhallstrom learningtoseecoloursbiologicallyrelevantvirtualstainingforadipocytecellimages AT philipjohnharrison learningtoseecoloursbiologicallyrelevantvirtualstainingforadipocytecellimages
_version_	1718374802136236032

Learning to see colours: Biologically relevant virtual staining for adipocyte cell images.

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