77 research outputs found
Digital synthesis of histological stains using micro-structured and multiplexed virtual staining of label-free tissue
Histological staining is a vital step used to diagnose various diseases and
has been used for more than a century to provide contrast to tissue sections,
rendering the tissue constituents visible for microscopic analysis by medical
experts. However, this process is time-consuming, labor-intensive, expensive
and destructive to the specimen. Recently, the ability to virtually-stain
unlabeled tissue sections, entirely avoiding the histochemical staining step,
has been demonstrated using tissue-stain specific deep neural networks. Here,
we present a new deep learning-based framework which generates
virtually-stained images using label-free tissue, where different stains are
merged following a micro-structure map defined by the user. This approach uses
a single deep neural network that receives two different sources of information
at its input: (1) autofluorescence images of the label-free tissue sample, and
(2) a digital staining matrix which represents the desired microscopic map of
different stains to be virtually generated at the same tissue section. This
digital staining matrix is also used to virtually blend existing stains,
digitally synthesizing new histological stains. We trained and blindly tested
this virtual-staining network using unlabeled kidney tissue sections to
generate micro-structured combinations of Hematoxylin and Eosin (H&E), Jones
silver stain, and Masson's Trichrome stain. Using a single network, this
approach multiplexes virtual staining of label-free tissue with multiple types
of stains and paves the way for synthesizing new digital histological stains
that can be created on the same tissue cross-section, which is currently not
feasible with standard histochemical staining methods.Comment: 19 pages, 5 figures, 2 table
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