Simple and robust deep learning approach for fast fluorescence lifetime imaging

Wang, Quan and Li, Yahui and Xiao, Dong and Zang, Zhenya and Jiao, Zi'ao and Chen, Yu and Li, David Day Uei (2022) Simple and robust deep learning approach for fast fluorescence lifetime imaging. Sensors, 22 (19). 7293. ISSN 1424-8220 (https://doi.org/10.3390/s22197293)

[thumbnail of Wang-etal-SENSORS-2022-Simple-and-robust-deep-learning-approach]
Preview
 Text. Filename: Wang_etal_SENSORS_2022_Simple_and_robust_deep_learning_approach.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (3MB)| Preview

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.

ORCID iDs

Wang, Quan, Li, Yahui, Xiao, Dong, Zang, Zhenya, Jiao, Zi'ao, Chen, Yu and Li, David Day Uei ORCID logoORCID: https://orcid.org/0000-0002-6401-4263;
CORE (COnnecting REpositories)