Simple and Robust Deep Learning Approach for Fast Fluorescence Lifetime Imaging

Autor:	Quan Wang, Yahui Li, Dong Xiao, Zhenya Zang, Zi’ao Jiao, Yu Chen, David Day Uei Li
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	fluorescence lifetime imaging (FLIM) deep learning imaging analysis Chemical technology TP1-1185
Zdroj:	Sensors, Vol 22, Iss 19, p 7293 (2022)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s22197293
Popis:	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.
Databáze:	Directory of Open Access Journals
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