Fig. 5: flimGANE enables precise metabolism quantification from low-photon-count autofluorescence data in live HeLa cells. | Communications Biology

Fig. 5: flimGANE enables precise metabolism quantification from low-photon-count autofluorescence data in live HeLa cells.

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

Fig. 5

a Intensity contrast images of FAD and NAD(P)H. b FLIM images of FAD and NAD(P)H generated by TD_LSE, TD_MLE, DFD_LSE, and flimGANE. c Pre-exponential factors (α1 and α2 = 1-α1, where α1 is the fraction of short lifetime component) of FAD and NAD(P)H obtained by different methods (error bars, standard deviation, n = 160,000 pixels). d FLIRR images show that flimGANE result best matches with TD_MLE result. e Intensity contrast images from a are normalized and segmented for mitochondria, cytoplasm, and nuclei. f Comparison of FLIRR results obtained from TD_LSE, TD_MLE, DFD_LSE, and flimGANE (solid line: based on the mitochondria images; dashed line: based on the whole-cell images without nuclei; n = 5 cells).

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