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

TLDR

Pathology relies on visual inspection of histochemically stained slides, with hematoxylin and eosin (H&E) serving as the gold standard, while special stains are employed for non‑neoplastic diseases to enhance contrast and diagnostic clarity. The study aims to demonstrate the utility of supervised learning–based computational stain transformation from H&E to Masson's Trichrome, periodic acid–Schiff, and Jones silver stains. The authors applied a supervised learning–based stain transformation network to kidney needle core biopsy sections to convert H&E images into virtual Masson's Trichrome, periodic acid–Schiff, and Jones silver stains. Evaluation by renal pathologists showed that virtual special stains generated by the network improved diagnosis of non‑neoplastic kidney diseases, were statistically equivalent in quality to conventional stains, and could be produced in under a minute per slide, offering significant time and cost savings.

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.