Deep learning-based multiplexed virtual staining of unlabeled tissue for micro-structured stain blending

We virtually generate multiple histological stains through a single deep-neural-network, using at its input autofluorescence images of the unlabeled tissue alongside a user-defined digital-staining-matrix. By feeding this digital-staining-matrix to the network, the user indicates which stain to apply on each pixel or region-of-interest, enabling virtual blending of multiple stains according to a desired micro-structure map. We demonstrated this technique by applying combinations of different stains (H&E, Masson’s Trichrome and Jones silver stain) on blindly-tested, unlabeled tissue sections. This technology avoids the histochemical staining process and enables newly-generated stains and stain-combinations to be used for inspection of label-free tissue microstructure.