Simple and Robust Deep Learning Approach for Fast Fluorescence Lifetime Imaging.

Journal: Sensors (Basel, Switzerland)
PMID: 36236390

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.

Authors

  • Quan Wang
    Laboratory of Surgical Oncology, Peking University People's Hospital, Peking University, Beijing, China.
  • Yahui Li
    Key Laboratory of Ultra-Fast Photoelectric Diagnostics Technology, Xi'an Institute of Optics and Precision Mechanics, Xi'an 710049, China.
  • Dong Xiao
    Information Science and Engineering School, Northeastern University, Shenyang 110819, China.
  • Zhenya Zang
  • Zi'ao Jiao
    Department of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RU, UK.
  • Yu Chen
    State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China.
  • David Day Uei Li
    Department of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UK.

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