Deep learning-based transformation of H&E stained tissues into special stains
By K. de Haan, Yijie Zhang, J. Zuckerman, Tairan Liu, A. Sisk, M. F. P. Diaz, Kuang-Yu Jen, Alexander Nobori, Sofia Liou, Sarah Zhang and 4 more. Nature Communications, 2020
316 citations recorded by the source corpus.
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. Performing multiple histological stains on a biopsy can be costly and time consuming. Here the authors present a method for the digital transformation of H&E stained tissue into special stains (e.g., PAS, Masson’s Trichrome and Jones silver stain), and demonstrate that it improves diagnoses over the use of H&E only.
Subfields: Artificial Intelligence, Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/dbd60e38888d3bdab0cc09a35bc86efe4b66501d
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- Bijie Bai
- Xilin Yang
- Yuzhu Li
- N. Pillar
- Aydogan Ozcan
- Yijie Zhang
Citations in the corpus
- Deep learning-enabled virtual histological staining of biological samples (2022, methodology): “For example, in the work of de Haan et al.21, three nephropathologists confirmed that the generation of the additional virtual special stains from existing H&E images improved the diagnosis of kidney diseases; similarly, Bai et al.31 involved three board-certified breast pathologists in validating…”