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

Journal: Nature communications
Published Date: Aug 12, 2021

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.

Authors

  • Kevin de Haan
    Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
  • Yijie Zhang
    Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, South China Normal University, Guangzhou, China; Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China. Electronic address: fanfandez@163.com.
  • Jonathan E Zuckerman
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, California.
  • Tairan Liu
    Department of Mechanical Engineering, Louisiana State University, Baton Rouge, LA, 70803, USA.
  • Anthony E Sisk
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Miguel F P Diaz
    Kaiser Permanente Los Angeles Medical Center, Department of Pathology, Los Angeles, CA, USA.
  • Kuang-Yu Jen
    Department of Pathology and Laboratory Medicine, School of Medicine, University of California, Davis, Davis, CA, United States.
  • Alexander Nobori
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Sofia Liou
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Sarah Zhang
    Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, United States of America.
  • Rana Riahi
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Yair Rivenson
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • W Dean Wallace
    Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
  • Aydogan Ozcan
    Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA.

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