Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE ( f luorescence l ifetime im aging based on G enerative A dversarial N etwork E stimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation ( TD_MLE ) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical. In this study, Chen et al. introduced a new deep learning-based method termed flimGANE to rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. flimGANE is particularly useful in fundamental biological research and clinical applications.