Simple and robust deep learning approach for fast fluorescence lifetime imaging

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Abstract

Fluorescence lifetime imaging (FLIM) is a powerful tool that provides unique quantitative information for biomedical research. In this study, we propose a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning (DL) algorithm named FLIM-MLP-Mixer for fast and robust FLIM analysis. The FLIM-MLP-Mixer has a simple network architecture yet a powerful learning ability from data. Compared with the traditional fitting and previously reported DL methods, the FLIM-MLP-Mixer shows superior performance in terms of accuracy and calculation speed, which has been validated using both synthetic and experimental data. All results indicate that our proposed method is well suited for accurately estimating lifetime parameters from measured fluorescence histograms, and it has great potential in various real-time FLIM applications.
Original languageEnglish
Article number7293
Number of pages10
JournalSensors
Volume22
Issue number19
DOIs
Publication statusPublished - 26 Sep 2022

Keywords

  • fluorescence lifetime imaging (FLIM)
  • deep learning
  • imaging analysis

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