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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
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    Cited by:

    1. 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.
    2. 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.
    3. 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.

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