Translator Disclaimer
Presentation
5 March 2021 Robust single-cell classification in hyperspectral stimulated raman scattering imaging by machine learning
Author Affiliations +
Abstract
Label-free phenotypic classification at a single-cell level is a challenging yet important task in cell biology. Stimulated Raman scattering (SRS) microscopy provides high chemical selectivity and sensitivity for label-free imaging of biological samples. With the capability to record hyperspectral SRS images with high-speed, mapping of biomolecules inside living cells enables label-free phenotyping. However, like all high-dimensional data, it remains challenging to fully exploit the excessive amount of information contained in hyperspectral data for single-cell analysis. Here, we developed and compared two machine-learning-based methods - the convolutional neural network and support vector machine - to automatically extract important features from high dimensional data and achieve a high-accuracy label-free single-cell classification. These methods serve as a robust approach to classify cells based on their molecular features, allowing unbiased, high-throughput data analysis.
Conference Presentation
© (2021) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hyeon Jeong Lee "Robust single-cell classification in hyperspectral stimulated raman scattering imaging by machine learning", Proc. SPIE 11648, Multiphoton Microscopy in the Biomedical Sciences XXI, 116480T (5 March 2021); https://doi.org/10.1117/12.2585438
PROCEEDINGS
PRESENTATION


SHARE
Advertisement
Advertisement
Back to Top