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

Area of Science:

• Biomedical Optics
• Computational Biology
• Machine Learning

Background:

• Fluorescence lifetime imaging (FLIM) is a crucial quantitative technique in biomedical research.
• Accurate and rapid analysis of FLIM data is essential for advancing biological insights and clinical applications.

Purpose of the Study:

• To develop a novel deep learning algorithm for efficient and robust FLIM data analysis.
• To introduce the FLIM-MLP-Mixer, a multi-layer-perceptron-based mixer algorithm, for enhanced FLIM analysis.

Main Methods:

• Implementation of a multi-layer-perceptron-based mixer (MLP-Mixer) deep learning architecture.
• Validation of the FLIM-MLP-Mixer algorithm using both synthetic and experimental FLIM datasets.
• Comparison of the proposed DL method against traditional fitting techniques and existing DL approaches.

Main Results:

• The FLIM-MLP-Mixer demonstrated superior accuracy and speed in lifetime parameter estimation compared to conventional methods.
• The algorithm's performance was robustly validated across diverse datasets.
• The network architecture, while simple, exhibited significant learning capabilities.

Conclusions:

• The FLIM-MLP-Mixer is highly effective for precise lifetime parameter estimation from fluorescence histograms.
• The developed algorithm shows significant promise for real-time applications in FLIM.
• This deep learning approach advances the capabilities of FLIM analysis in biomedical research.