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7 March 2022 Single-cell analysis of autofluorescence lifetime images
Alex J. Walsh, Elizabeth Cardona, Samantha Morganti, Nianchao Wang, Linghao Hu, Addison Threet
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Proceedings Volume PC11965, Multiphoton Microscopy in the Biomedical Sciences XXII; PC119650H (2022) https://doi.org/10.1117/12.2609806
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
Multiphoton fluorescence lifetime imaging of the metabolic coenzymes reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD) allows quantification of cellular metabolism. Due to the link between cellular metabolism and cell function, autofluorescence lifetime imaging provides many features for identification of cells with different phenotypes. Segmentation of multiphoton fluorescence lifetime images allows analysis of data at a single-cell level and quantification of cellular heterogeneity. In this study, Gaussian distribution modeling and machine learning classification algorithms are used for the identification of rare cells within autofluorescence lifetime image data.
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Alex J. Walsh, Elizabeth Cardona, Samantha Morganti, Nianchao Wang, Linghao Hu, and Addison Threet "Single-cell analysis of autofluorescence lifetime images", Proc. SPIE PC11965, Multiphoton Microscopy in the Biomedical Sciences XXII, PC119650H (7 March 2022); https://doi.org/10.1117/12.2609806
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KEYWORDS
Multiphoton fluorescence microscopy
Image analysis
Tumors
Breast cancer
Data modeling
Image processing algorithms and systems
Image segmentation
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