Machine learning augmented cell imaging and classification with stimulated Raman scattering microscopy (Conference Presentation)

Dan Fu

doi:10.1117/12.2649567

15 March 2023 Machine learning augmented cell imaging and classification with stimulated Raman scattering microscopy (Conference Presentation)

Dan Fu

Author Affiliations +

Proceedings Volume PC12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023; PC123920L (2023) https://doi.org/10.1117/12.2649567
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Stimulated Raman scattering (SRS) microscopy is a powerful chemical imaging tool for visualizing biomolecule distribution in biological cells and tissues. Recent research has focused on SRS imaging in the C-H region due to the strong signals from lipids and proteins. However, these signals are regarded as non-specific. To improve the specificity of C-H imaging, we sought to use advanced machine learning to extract hidden information from C-H SRS imaging. This is possible because cells in tissue often have distinct sizes, shapes, and compositions. In this talk, I will present our recent efforts on machine learning/deep learning augmented cell imaging and classification with SRS. This approach potentially enables label-free mapping and tracking of different cells in various tissue.

Conference Presentation

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Dan Fu "Machine learning augmented cell imaging and classification with stimulated Raman scattering microscopy (Conference Presentation)", Proc. SPIE PC12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023, PC123920L (15 March 2023); https://doi.org/10.1117/12.2649567

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KEYWORDS

Machine learning

Microscopy

Raman scattering

Tissues

Imaging spectroscopy

Mode conditioning cables

Pathology

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