Deep-learning autoencoders for unsupervised BCARS chemical imaging

Ryan Muddiman; Bryan Hennelly

doi:10.1117/12.2687670

27 November 2023 Deep-learning autoencoders for unsupervised BCARS chemical imaging

Ryan Muddiman, Bryan Hennelly

Proceedings Volume 12770, Optics in Health Care and Biomedical Optics XIII; 1277033 (2023) https://doi.org/10.1117/12.2687670
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

The application of Broadband CARS to cell imaging studies has thus far been limited to those where high contrast features are present, such as lipids and exogenously introduced tags. This is due to the inherent low SNR obtained in BCARS from the low density of oscillators in single cells coupled with the non-resonant background present in all media which distorts the measured signal. In this paper, we show that an autoencoder which we named VECTOR2, trained on simulated spectra, can accurately perform NRB removal of recorded BCARS images of unstained biological specimen. This allows cell imaging comparable in time to spontaneous Raman imaging with high bandwidth and resolution. The introduction of standard baseline flattening prior to NRB removal preserves the image structure while removing artefacts from raster scanning and optical noise. This results in a hyperspectral image of the NRB-free BCARS signal which is linear in the sample concentration and has a spectrum that is very similar to the spontaneous Raman spectrum.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ryan Muddiman and Bryan Hennelly "Deep-learning autoencoders for unsupervised BCARS chemical imaging", Proc. SPIE 12770, Optics in Health Care and Biomedical Optics XIII, 1277033 (27 November 2023); https://doi.org/10.1117/12.2687670

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