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Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning

Nature Biomedical Engineering volume 3, pages 466–477 (2019)Cite this article

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Abstract

The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wide-field autofluorescence images of unlabelled tissue sections into images that are equivalent to the bright-field images of histologically stained versions of the same samples. A blind comparison, by board-certified pathologists, of this virtual staining method and standard histological staining using microscopic images of human tissue sections of the salivary gland, thyroid, kidney, liver and lung, and involving different types of stain, showed no major discordances. The virtual-staining method bypasses the typically labour-intensive and costly histological staining procedures, and could be used as a blueprint for the virtual staining of tissue images acquired with other label-free imaging modalities.

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Fig. 1: Deep-learning-based virtual histology staining using autofluorescence of unstained tissue.
Fig. 2: Virtual staining GAN architecture.
Fig. 3: Virtual staining results match the H&E- and Jones-stained images.
Fig. 4: Virtual staining results match the Masson’s trichrome stain for liver and lung tissue sections.
Fig. 5: Virtual staining reduces staining variability.
Fig. 6: Accelerated convergence is achieved using transfer learning.
Fig. 7: Melanin inference using multiple autofluorescence channels.

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Code availability

The deep-learning models used in this work employ standard libraries and scripts that are publicly available in TensorFlow. The trained network models for Masson’s trichrome stain (liver) and Jones stain (kidney), alongside sample test-image data are available through a Fiji-based plugin at https://github.com/whd0121/ImageJ-VirtualStain (Fiji can be downloaded at: https://imagej.net/Fiji/Downloads). The Fiji Grid/Collection stitching plugin was used to perform FOVs stitching. The inference (testing) software has been adapted to Fiji. MATLAB was used for the shading correction as well as the registration steps (coarse matching, global registration and local registration). Python based on the TensorFlow library was used to implement both the initial CNN used for image registration as well as the CNN used to produce the final virtually stained images. Our custom training codes are proprietary (and managed by the UCLA Office of Intellectual Property) and are not publicly available.

Data availability

The authors declare that all data supporting the results in this study are available within the paper and the Supplementary Information.

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Acknowledgements

The Ozcan Research Group at UCLA acknowledges the support of the NSF Engineering Research Center (PATHS-UP), the Army Research Office, the NSF CBET Division Biophotonics Program, the National Institutes of Health (NIH, R21EB023115), HHMI, Vodafone Americas Foundation, the Mary Kay Foundation, and the Steven & Alexandra Cohen Foundation. The authors also acknowledge the Translational Pathology Core Laboratory and the Histology Laboratory at UCLA for their assistance with the sample preparation and staining. The authors acknowledge the time and effort of S. French, B.D. Cone, A. Nobori and C.M. Lee of the UCLA Department of Pathology and Laboratory Medicine for their evaluations; the assistance of R. Gao in preparing the ImageJ plugin and of R. Suh at the UCLA Department of Radiology for his help with Fig. 1.

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Author notes

  1. These authors contributed equally: Yair Rivenson, Hongda Wang.

Authors and Affiliations

  1. Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA

    Yair Rivenson, Hongda Wang, Zhensong Wei, Kevin de Haan, Yibo Zhang, Yichen Wu, Harun Günaydın & Aydogan Ozcan

  2. Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA

    Yair Rivenson, Hongda Wang, Kevin de Haan, Yibo Zhang, Yichen Wu & Aydogan Ozcan

  3. California NanoSystems Institute, University of California, Los Angeles, Los Angeles, CA, USA

    Yair Rivenson, Hongda Wang, Kevin de Haan, Yibo Zhang, Yichen Wu & Aydogan Ozcan

  4. Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Jonathan E. Zuckerman, Thomas Chong, Anthony E. Sisk, Lindsey M. Westbrook & W. Dean Wallace

  5. Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Aydogan Ozcan

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Contributions

Y.R. and A.O. conceived the research, H.W. and Y.R. conducted the experiments, and Y.R., Z.W., K.d.H., H.G., Y.Z. and H.W. processed the data. W.D.W. directed the clinical aspects of the research. J.E.Z., T.C., A.E.S. and L.M.W. performed diagnosis and stain efficacy assessment on the virtual and histologically stained slides. Y.R., H.W., Z.W., K.d.H., Y.Z., W.D.W. and A.O. prepared the manuscript and all authors contributed to the manuscript. A.O. supervised the research.

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Correspondence to Yair Rivenson or Aydogan Ozcan.

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Competing interests

A.O., Y.R., H.W. and Z.W. have applied for a patent (US application number: 62651005) related to the work reported in this manuscript.

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Rivenson, Y., Wang, H., Wei, Z. et al. Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning. Nat Biomed Eng 3, 466–477 (2019). https://doi.org/10.1038/s41551-019-0362-y

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