Deep learning for analysis of hyperspectral stimulated Raman scattering images

Bryce Manifold; Dan Fu

doi:10.1117/12.2609800

2 March 2022 Deep learning for analysis of hyperspectral stimulated Raman scattering images

Bryce Manifold, Dan Fu

Proceedings Volume PC11973, Advanced Chemical Microscopy for Life Science and Translational Medicine 2022; PC119730Y (2022) https://doi.org/10.1117/12.2609800
Event: SPIE BiOS, 2022, San Francisco, California, United States

Abstract

Hyperspectral stimulated Raman scattering (hsSRS) microscopy provides rich chemical and spatial information not regularly available to traditional microscopy methods. However, analysis of hsSRS images is often confounded by convolved and overlapping spectral features requiring use of machine learning methods to extract information. Here, we demonstrate the use of our recently published deep learning architecture (the U-within-U-Net) designed for hyperspectral images on hsSRS images. We demonstrate segmentation, classification, and prediction of orthogonal imaging modalities. We also show the architecture is applicable to other hyperspectral imaging modalities with implications for remote sensing and mass spectrometry imaging.

Conference Presentation

Citation Download Citation

Bryce Manifold and Dan Fu "Deep learning for analysis of hyperspectral stimulated Raman scattering images", Proc. SPIE PC11973, Advanced Chemical Microscopy for Life Science and Translational Medicine 2022, PC119730Y (2 March 2022); https://doi.org/10.1117/12.2609800

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