Machine learning for faster and smarter fluorescence lifetime imaging microscopy

5 Aug 2020  ·  Varun Mannam, Yide Zhang, Xiao-Tong Yuan, Cara Ravasio, Scott S. Howard ·

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy. However, at present, the calculation, analysis, and interpretation of FLIM is a complex, slow, and computationally expensive process. Machine learning (ML) techniques are well suited to extract and interpret measurements from multi-dimensional FLIM data sets with substantial improvement in speed over conventional methods. In this topical review, we first discuss the basics of FILM and ML. Second, we provide a summary of lifetime extraction strategies using ML and its applications in classifying and segmenting FILM images with higher accuracy compared to conventional methods. Finally, we discuss two potential directions to improve FLIM with ML with proof of concept demonstrations.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Image Denoising FMD NOise2NOise PSNR 8-10dB # 1


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