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.

本研究应用有监督学习的计算机染色转换技术，将经典的血红蛋白与嗜酸性染色（H&E）转换为肾脏穿刺活组织检测中的不同特殊染色，从而改善了多种非肿瘤性肾脏疾病的初步诊断质量，并减少了医疗系统和患者的时间和成本负担。