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Machine learning for faster and smarter fluorescence lifetime imaging microscopy

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Document pages: 25 pages

Abstract: Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique inbiomedical research that uses the fluorophore decay rate to provide additionalcontrast in fluorescence microscopy. However, at present, the calculation,analysis, and interpretation of FLIM is a complex, slow, and computationallyexpensive process. Machine learning (ML) techniques are well suited to extractand interpret measurements from multi-dimensional FLIM data sets withsubstantial improvement in speed over conventional methods. In this topicalreview, we first discuss the basics of FILM and ML. Second, we provide asummary of lifetime extraction strategies using ML and its applications inclassifying and segmenting FILM images with higher accuracy compared toconventional methods. Finally, we discuss two potential directions to improveFLIM with ML with proof of concept demonstrations.

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