IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-25221-2.html
   My bibliography  Save this article

Deep learning-based transformation of H&E stained tissues into special stains

Author

Listed:
  • Kevin Haan

    (University of California
    University of California
    University of California)

  • Yijie Zhang

    (University of California
    University of California
    University of California)

  • Jonathan E. Zuckerman

    (University of California, Los Angeles)

  • Tairan Liu

    (University of California
    University of California
    University of California)

  • Anthony E. Sisk

    (University of California, Los Angeles)

  • Miguel F. P. Diaz

    (Department of Pathology)

  • Kuang-Yu Jen

    (University of California at Davis)

  • Alexander Nobori

    (University of California, Los Angeles)

  • Sofia Liou

    (University of California, Los Angeles)

  • Sarah Zhang

    (University of California, Los Angeles)

  • Rana Riahi

    (University of California, Los Angeles)

  • Yair Rivenson

    (University of California
    University of California
    University of California)

  • W. Dean Wallace

    (Keck School of Medicine of USC)

  • Aydogan Ozcan

    (University of California
    University of California
    University of California
    University of California)

Abstract

Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson’s Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost.

Suggested Citation

  • Kevin Haan & Yijie Zhang & Jonathan E. Zuckerman & Tairan Liu & Anthony E. Sisk & Miguel F. P. Diaz & Kuang-Yu Jen & Alexander Nobori & Sofia Liou & Sarah Zhang & Rana Riahi & Yair Rivenson & W. Dean , 2021. "Deep learning-based transformation of H&E stained tissues into special stains," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25221-2
    DOI: 10.1038/s41467-021-25221-2
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-25221-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-021-25221-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xilin Yang & Bijie Bai & Yijie Zhang & Musa Aydin & Yuzhu Li & Sahan Yoruc Selcuk & Paloma Casteleiro Costa & Zhen Guo & Gregory A. Fishbein & Karine Atlan & William Dean Wallace & Nir Pillar & Aydoga, 2024. "Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Seungmin Lee & Jeong Soo Park & Hyowon Woo & Yong Kyoung Yoo & Dongho Lee & Seok Chung & Dae Sung Yoon & Ki- Baek Lee & Jeong Hoon Lee, 2024. "Rapid deep learning-assisted predictive diagnostics for point-of-care testing," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    3. Shu Wang & Xiaoxiang Liu & Yueying Li & Xinquan Sun & Qi Li & Yinhua She & Yixuan Xu & Xingxin Huang & Ruolan Lin & Deyong Kang & Xingfu Wang & Haohua Tu & Wenxi Liu & Feng Huang & Jianxin Chen, 2023. "A deep learning-based stripe self-correction method for stitched microscopic images," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    4. Yuzhu Li & Nir Pillar & Jingxi Li & Tairan Liu & Di Wu & Songyu Sun & Guangdong Ma & Kevin Haan & Luzhe Huang & Yijie Zhang & Sepehr Hamidi & Anatoly Urisman & Tal Keidar Haran & William Dean Wallace , 2024. "Virtual histological staining of unlabeled autopsy tissue," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-25221-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.