SofiaKapsiani/FLIMngo
Deep learning for fluorescence lifetime predictions
FLIMngo helps researchers in microscopy and bioimaging quickly determine fluorescence lifetimes from raw TCSPC-FLIM data. It takes in 256x256 pixel images with 256 time dimensions and outputs predicted fluorescence lifetimes. This tool is for scientists using advanced microscopy techniques to study biological processes, allowing for faster analysis than traditional methods.
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Use this if you need to rapidly predict fluorescence lifetimes from TCSPC-FLIM data, especially when dealing with low photon counts or high-throughput in vivo imaging.
Not ideal if your raw data has significantly different spatial or time dimensions than 256x256 pixels and 256 time channels, or if your IRF or lifetimes are outside the 0.1-10 ns range.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Jun 17, 2025
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