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20 August 2020 Multiplexed and micro-structured virtual staining of unlabeled tissue using deep learning
Kevin de Haan, Yijie Zhang, Yair Rivenson, Jingxi Li, Apostolos Delis, Aydogan Ozcan
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Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114690D (2020) https://doi.org/10.1117/12.2567507
Event: SPIE Nanoscience + Engineering, 2020, Online Only
Abstract
We present a method to generate multiple virtual stains on an image of label-free tissue using a single deep neural network according to a user-defined micro-structure map. The input to this network comes from two sources: (i) autofluorescence microscopy images of the unlabeled tissue, (ii) a user-defined digital-staining-matrix. This digital-staining-matrix indicates which stain is to be virtually-generated for each pixel, and can be used to create a micro-structured stain map, or virtually blend stains together. We experimentally validated this approach using blind-testing of label-free kidney tissue sections, and successfully generated combinations of H and E, Masson’s Trichome stain, and Jones silver stain.
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Kevin de Haan, Yijie Zhang, Yair Rivenson, Jingxi Li, Apostolos Delis, and Aydogan Ozcan "Multiplexed and micro-structured virtual staining of unlabeled tissue using deep learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690D (20 August 2020); https://doi.org/10.1117/12.2567507
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KEYWORDS
Tissues
Multiplexing
Neural networks
Chemical analysis
Chemical reactions
Diagnostics
Kidney
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