Deep learning-based transformation of the H&E stain into special stains
- URL: http://arxiv.org/abs/2008.08871v2
- Date: Fri, 13 Aug 2021 00:12:07 GMT
- Title: Deep learning-based transformation of the H&E stain into special stains
- Authors: Kevin de 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 Wallace, Aydogan Ozcan
- Abstract summary: We show the utility of supervised learning-based computational stain transformation from H&E to different special stains using tissue sections from kidney needle core biopsies.
Results: The quality of the special stains generated by the stain transformation network was statistically equivalent to those generated through standard histochemical staining.
- Score: 44.38127957263123
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Pathology is practiced by visual inspection of histochemically stained
slides. Most commonly, the hematoxylin and eosin (H&E) stain is used in the
diagnostic workflow and it is the gold standard for cancer diagnosis. However,
in many cases, especially for non-neoplastic diseases, additional "special
stains" are used to provide different levels of contrast and color to tissue
components and allow pathologists to get a clearer diagnostic picture. In this
study, we demonstrate the utility of supervised learning-based computational
stain transformation from H&E to different special stains (Masson's Trichrome,
periodic acid-Schiff and Jones silver stain) using tissue sections from kidney
needle core biopsies. Based on evaluation by three renal pathologists, followed
by adjudication by a fourth renal pathologist, we show that the generation of
virtual special stains from existing H&E images improves the diagnosis in
several non-neoplastic kidney diseases sampled from 58 unique subjects. A
second study performed by three pathologists found that the quality of the
special stains generated by the stain transformation network was statistically
equivalent to those generated through standard histochemical staining. As the
transformation of H&E images into special stains can be achieved within 1 min
or less per patient core specimen slide, this stain-to-stain transformation
framework can improve the quality of the preliminary diagnosis when additional
special stains are needed, along with significant savings in time and cost,
reducing the burden on healthcare system and patients.
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