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Segmentation of stimulated raman microscopy images using a 1D convolutional neural network

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1109/PN56061.2022.9908347
AuthorSearch for: 1; Search for: 2; Search for: 3; Search for: 4
Affiliation
  1. National Research Council of Canada. Construction
  2. Few-Cycle Inc.
  3. National Research Council of Canada. Metrology Research Centre
  4. University of Ottawa. Department of Physics
FormatText, Article
Conference2022 Photonics North (PN), May 24-26, 2022, Niagara Falls, Ontario, Canada
Subjectvibrations; training; image segmentation; optical microscopy; microscopy; Raman scattering; optical imaging
Abstract
Stimulated Raman Scattering (SRS) microscopy is a powerful nonlinear optical imaging technique deriving contrast from Raman active molecular vibrations. We demonstrate, using a supervised convolutional neural network (RM-Net), the creation of chemical maps from hyperspectral Stimulated Raman Scattering images. Using a limited number (800) of training spectra, the trained RM-Net model was successfully applied to new hyperspectral images without compromising accuracy.
Publication date
PublisherIEEE
In
  • 2022 Photonics North (PN), 9908347 (24 May 2022).
LanguageEnglish
Peer reviewedYes
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Record identifier35fdf9c6-fd78-4fa0-99f7-572a44e3d5ec
Record created2023-06-30
Record modified2023-06-30
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