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Open Source High Content Analysis Utilizing Automated Fluorescence Lifetime Imaging Microscopy
Published on: January 18, 2017
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Fast fit-free analysis of fluorescence lifetime imaging via deep learning
Jason T Smith1, Ruoyang Yao2, Nattawut Sinsuebphon2
1Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180; smithj28@rpi.edu intesx@rpi.edu.
Summary
We developed a deep learning method, FLI-Net, for fast and accurate fluorescence lifetime imaging (FLI). This fit-free approach quantifies fluorescence decays in real-time for cells and animals, enhancing biomedical research.
Area of Science:
- Biomedical Optics
- Molecular Biology
- Deep Learning Applications
Background:
- Fluorescence lifetime imaging (FLI) offers valuable quantitative data in life sciences.
- Traditional FLI analysis requires complex, time-consuming data-fitting methods.
- There is a need for faster, more accessible FLI quantification techniques.
Purpose of the Study:
- To introduce a novel, fit-free deep learning approach for rapid FLI image formation.
- To develop and validate a deep neural network (DNN) for quantifying fluorescence decays.
- To enable real-time, quantitative FLI analysis across various experimental setups.
Main Methods:
- Developed a deep neural network architecture named fluorescence lifetime imaging network (FLI-Net).
- Trained FLI-Net for visible and near-infrared (NIR) FLI microscopy (FLIM) and macroscopy (MFLI).
- Validated FLI-Net using quantitative microscopic and preclinical studies.
Main Results:
- FLI-Net accurately quantifies spatially resolved lifetime-based parameters from fluorescence decays.
- The network performs effectively across visible and NIR spectra and different data acquisition technologies.
- Demonstrated real-time, accurate quantification of fluorescence lifetimes in cells and intact animals.
Conclusions:
- FLI-Net provides a robust, fit-free solution for quantitative FLI.
- This deep learning framework significantly accelerates FLI data analysis.
- FLI-Net enhances the reproducibility and impact of FLI in biomedical research and clinical translation.
