Segmentation of hyperspectral stimulated Raman images using deep learning techniques (Conference Presentation)

Adiel Felsen; Nikolas Burzynski; Soumit Saha; Yuhao Yuan; David Reitano; Zhibo Wang; Khalid A. Sethi; Kenneth Chiu; Fake Lu

doi:10.1117/12.2650882

15 March 2023 Segmentation of hyperspectral stimulated Raman images using deep learning techniques (Conference Presentation)

Adiel Felsen, Nikolas Burzynski, Soumit Saha, Yuhao Yuan, David Reitano, Zhibo Wang, Khalid A. Sethi, Kenneth Chiu, Fake Lu

Author Affiliations +

Proceedings Volume PC12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023; PC1239212 (2023) https://doi.org/10.1117/12.2650882
Event: SPIE BiOS, 2023, San Francisco, California, United States

Abstract

Stimulated Raman scattering (SRS) imaging of fresh tissue is an emerging approach to render label-free pathology and diagnosis based on the chemical contrasts of native biomolecules. However, the contrast of cell nuclei is often too weak to perform reliable segmentation and quantification. To advance the development of label-free digital pathology with SRS, we explored three deep learning-based techniques with multicolor and hyperspectral SRS imaging data, including U-Net, Mask R-CNN, and Autoencoder. Our results show that a combination of machine learning and SRS imaging is a promising pathway to transforming the methodology of SRS label-free pathology for real clinical use.

Conference Presentation

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Adiel Felsen, Nikolas Burzynski, Soumit Saha, Yuhao Yuan, David Reitano, Zhibo Wang, Khalid A. Sethi, Kenneth Chiu, and Fake Lu "Segmentation of hyperspectral stimulated Raman images using deep learning techniques (Conference Presentation)", Proc. SPIE PC12392, Advanced Chemical Microscopy for Life Science and Translational Medicine 2023, PC1239212 (15 March 2023); https://doi.org/10.1117/12.2650882

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