Abstract

Deep neural networks have been used to map multi-modal, multi-photon microscopy measurements of a label-free tissue sample to its corresponding histologically stained brightfield microscope colour image. It is shown that the extra structural and functional contrasts provided by using two source modes, namely two-photon excitation microscopy and fluorescence lifetime imaging, result in a more faithful reconstruction of the target haematoxylin and eosin stained mode. This modal mapping procedure can aid histopathologists, since it provides access to unobserved imaging modalities, and translates the high-dimensional numerical data generated by multi-modal, multi-photon microscopy into traditionally accepted visual forms. Furthermore, by combining the strengths of traditional chemical staining and modern multi-photon microscopy techniques, modal mapping enables label-free, non-invasive studies of in vivo tissue samples or intravital microscopic imaging inside living animals. The results show that modal co-registration and the inclusion of spatial variations increase the visual accuracy of the mapped results.

Original languageEnglish (US)
Article number355205
Pages (from-to)1339-1350
Number of pages12
JournalBiomedical Optics Express
Volume10
Issue number3
DOIs
StatePublished - Mar 1 2019

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics

Fingerprint Dive into the research topics of 'Digital staining through the application of deep neural networks to multi-modal multi-photon microscopy'. Together they form a unique fingerprint.

  • Cite this