Deep convolutional neural network recovers pure absorbance spectra from highly scatter-distorted spectra of cells.
Mie scattering
convolutional neural networks
deep learning
infrared spectroscopy
Journal
Journal of biophotonics
ISSN: 1864-0648
Titre abrégé: J Biophotonics
Pays: Germany
ID NLM: 101318567
Informations de publication
Date de publication:
12 2020
12 2020
Historique:
received:
27
05
2020
revised:
21
08
2020
accepted:
21
08
2020
pubmed:
28
8
2020
medline:
24
6
2021
entrez:
27
8
2020
Statut:
ppublish
Résumé
Infrared spectroscopy of cells and tissues is prone to Mie scattering distortions, which grossly obscure the relevant chemical signals. The state-of-the-art Mie extinction extended multiplicative signal correction (ME-EMSC) algorithm is a powerful tool for the recovery of pure absorbance spectra from highly scatter-distorted spectra. However, the algorithm is computationally expensive and the correction of large infrared imaging datasets requires weeks of computations. In this paper, we present a deep convolutional descattering autoencoder (DSAE) which was trained on a set of ME-EMSC corrected infrared spectra and which can massively reduce the computation time for scatter correction. Since the raw spectra showed large variability in chemical features, different reference spectra matching the chemical signals of the spectra were used to initialize the ME-EMSC algorithm, which is beneficial for the quality of the correction and the speed of the algorithm. One DSAE was trained on the spectra, which were corrected with different reference spectra and validated on independent test data. The DSAE outperformed the ME-EMSC correction in terms of speed, robustness, and noise levels. We confirm that the same chemical information is contained in the DSAE corrected spectra as in the spectra corrected with ME-EMSC.
Identifiants
pubmed: 32844585
doi: 10.1002/jbio.202000204
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e202000204Informations de copyright
© 2020 The Authors. Journal of Biophotonics published by WILEY-VCH GmbH.
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