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5 March 2021 Deep learning-based multiplexed virtual staining of unlabeled tissue for micro-structured stain blending
Kevin De Haan, Yijie Zhang, Yair Rivenson, Jingxi Li, Aydogan Ozcan
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Proceedings Volume 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021; 116550A (2021) https://doi.org/10.1117/12.2579426
Event: SPIE BiOS, 2021, Online Only
Abstract
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.
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Kevin De Haan, Yijie Zhang, Yair Rivenson, Jingxi Li, and Aydogan Ozcan "Deep learning-based multiplexed virtual staining of unlabeled tissue for micro-structured stain blending", Proc. SPIE 11655, Label-free Biomedical Imaging and Sensing (LBIS) 2021, 116550A (5 March 2021); https://doi.org/10.1117/12.2579426
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KEYWORDS
Tissues
Multiplexing
Neural networks
Chemical analysis
Inspection
Silver
Standards development
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