Artificial neural network approaches for fluorescence lifetime imaging techniques

Gang Wu, Thomas Nowotny, Yongliang Zhang, Hongqi Yu, David Day-Uei Li

Research output: Contribution to journalArticle

11 Citations (Scopus)
91 Downloads (Pure)


A novel high-speed fluorescence lifetime imaging (FLIM) analysis method based on artificial neural networks (ANN) has been proposed. The proposed ANN-FLIM method does not require iterative searching procedures or initial conditions, which are usually required for traditional FLIM methods. In terms of image generation, ANN-FLIM is free from iterative computations and able to generate lifetime images at least 180-fold faster than conventional least squares curve-fitting approaches. The advantages of ANN-FLIM were demonstrated on both synthesized and experimental data, showing that it has great potential to fuel current revolutions in rapid FLIM technologies.
Original languageEnglish
Pages (from-to)2561-2564
Number of pages4
JournalOptics Letters
Issue number11
Publication statusPublished - 25 May 2016


  • fluorescence lifetime imaging microscopy
  • FLIM
  • fluorophores


Smart solid-state Raman spectrometers

Li, D. & Lin, S.

Royal Society


Project: Research

GPU-enhanced Biomedical Image Analysis

Li, D., Nowotny, T. & Wu, G.


Project: Research


Cite this