Deep learning enables rapid and robust analysis of fluorescence lifetime imaging in photon-starved conditions
Y.-I. Chen, Y.-J. Chang, S.-C. Liao, T. D. Nguyen, J. Yang, Y.-A. Kuo, S. Hong, Y.-L. Liu, H. G. Rylander, S. R. Santacruz, T. E. Yankeelov, H.-C. Yeh
This paper introduces flimGANE, a deep learning method that rapidly and accurately generates FLIM images in low-photon conditions, outperforming traditional methods in speed and analysis accuracy.
bioRxiv · Dec 2, 2020 · PreprintCell Biology