Pre-processing visualization of hyperspectral fluorescent data with Spectrally Encoded Enhanced Representations

Wen Shi, Daniel E. S. Koo, Masahiro Kitano, Hsiao J. Chiang, Le A. Trinh, Gianluca Turcatel, Benjamin Steventon, Cosimo Arnesano, David Warburton, Scott E. Fraser and Francesco Cutrale ()
Additional contact information
Wen Shi: University of Southern California
Daniel E. S. Koo: University of Southern California
Masahiro Kitano: University of Southern California
Hsiao J. Chiang: University of Southern California
Le A. Trinh: University of Southern California
Gianluca Turcatel: Children’s Hospital
Benjamin Steventon: University of Cambridge
Cosimo Arnesano: University of Southern California
David Warburton: Children’s Hospital
Scott E. Fraser: University of Southern California
Francesco Cutrale: University of Southern California

Nature Communications, 2020, vol. 11, issue 1, 1-15

Abstract: Abstract Hyperspectral fluorescence imaging is gaining popularity for it enables multiplexing of spatio-temporal dynamics across scales for molecules, cells and tissues with multiple fluorescent labels. This is made possible by adding the dimension of wavelength to the dataset. The resulting datasets are high in information density and often require lengthy analyses to separate the overlapping fluorescent spectra. Understanding and visualizing these large multi-dimensional datasets during acquisition and pre-processing can be challenging. Here we present Spectrally Encoded Enhanced Representations (SEER), an approach for improved and computationally efficient simultaneous color visualization of multiple spectral components of hyperspectral fluorescence images. Exploiting the mathematical properties of the phasor method, we transform the wavelength space into information-rich color maps for RGB display visualization. We present multiple biological fluorescent samples and highlight SEER’s enhancement of specific and subtle spectral differences, providing a fast, intuitive and mathematical way to interpret hyperspectral images during collection, pre-processing and analysis.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-020-14486-8 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-14486-8

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-020-14486-8

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().