Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

Yijie Zhang; Kevin de Haan; Yair Rivenson; Jingxi Li; Apostolos Delis; Aydogan Ozcan

doi:10.1038/s41377-020-0315-y

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Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue

Original Articles

- Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue
- Light: Science & Applications Vol. 9, Issue 3, Pages: 751-763(2020)
- Affiliations：
  
  1.Electrical and Computer Engineering Department, University of California, Los Angeles, CA 90095, USA
  2.Bioengineering Department, University of California, Los Angeles, CA 90095, USA
  3.California NanoSystems Institute (CNSI), University of California, Los Angeles, CA 90095, USA
  4.Department of Computer Science, University of California, Los Angeles, CA 90095, USA
  5.Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
- Author bio：
  
  Yair Rivenson (rivensonyair@ucla.edu)
  Aydogan Ozcan (ozcan@ucla.edu)
- Funds：
- DOI：10.1038/s41377-020-0315-y
  CLC：
- Published：2020，
  
  Published Online：06 May 2020，
  
  Received：26 March 2020，
  
  Revised：14 April 2020，
  
  Accepted：15 April 2020
- Accepted：
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Zhang, Y. J. et al. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue. Light: Science & Applications, 9, 751-763 (2020).
DOI：

Zhang, Y. J. et al. Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue. Light: Science & Applications, 9, 751-763 (2020). DOI： 10.1038/s41377-020-0315-y.

摘要

Abstract

Histological staining is a vital step in diagnosing various diseases and has been used for more than a century to provide contrast in tissue sections

rendering the tissue constituents visible for microscopic analysis by medical experts. However

this process is time consuming

labour intensive

expensive and destructive to the specimen. Recently

the ability to virtually stain unlabelled tissue sections

entirely avoiding the histochemical staining step

has been demonstrated using tissue-stain-specific deep neural networks. Here

we present a new deep-learning-based framework that generates virtually stained images using label-free tissue images

in which different stains are merged following a micro-structure map defined by the user. This approach uses a single deep neural network that receives two different sources of information as its input: (1) autofluorescence images of the label-free tissue sample and (2) a "digital staining matrix"

which represents the desired microscopic map of the different stains to be virtually generated in the same tissue section. This digital staining matrix is also used to virtually blend existing stains

digitally synthesizing new histological stains. We trained and blindly tested this virtual-staining network using unlabelled kidney tissue sections to generate micro-structured combinations of haematoxylin and eosin (H & E)

Jones' silver stain

and Masson's trichrome stain. Using a single network

this approach multiplexes the virtual staining of label-free tissue images with multiple types of stains and paves the way for synthesizing new digital histological stains that can be created in the same tissue cross section

which is currently not feasible with standard histochemical staining methods.

关键词

Keywords

references

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