Open AccessDigital staining through the application of deep neural networks to multi-modal multi-photon microscopy
Author(s) -
Navid Borhani,
Andrew J. Bower,
Stephen A. Boppart,
Demetri Psaltis
Publication year - 2019
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.10.001339
Subject(s) - microscopy , two photon excitation microscopy , optics , haematoxylin , microscope , eosin , optical sectioning , modal , fluorescence microscope , computer science , artificial intelligence , materials science , computer vision , biomedical engineering , staining , fluorescence , physics , pathology , medicine , polymer chemistry
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.