1. Deep learning-based transformation of H&E stained tissues into special stains.
- Author
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de Haan, Kevin, Zhang, Yijie, Zuckerman, Jonathan E, Liu, Tairan, Sisk, Anthony E, Diaz, Miguel FP, Jen, Kuang-Yu, Nobori, Alexander, Liou, Sofia, Zhang, Sarah, Riahi, Rana, Rivenson, Yair, Wallace, W Dean, and Ozcan, Aydogan
- Subjects
Kidney ,Humans ,Kidney Diseases ,Diagnosis ,Computer-Assisted ,Diagnosis ,Differential ,Staining and Labeling ,Sensitivity and Specificity ,Reproducibility of Results ,Pathology ,Clinical ,Algorithms ,Reference Standards ,Coloring Agents ,Biopsy ,Large-Core Needle ,Deep Learning - 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.
- Published
- 2021