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20 August 2020 Multiplexed and micro-structured virtual staining of unlabeled tissue using deep learning
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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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© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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