One-dimensional deep learning architecture for fast fluorescence lifetime imaging

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We present a hardware-friendly deep learning architecture with one-dimensional convolutional neural networks (1D CNN) for fast analyzing fluorescence lifetime imaging (FLIM) data. A 1D CNN shows unparalleled advantages; they are more straightforward, quicker to train, and faster than high dimensional CNNs. 1D CNNs can be easily applied to multi-exponential fluorescence decay models. Compared with traditional least-square methods, superior performances of 1D CNNs on fluorescence lifetime image reconstruction have been validated using simulated data. We also employ the proposed 1D CNN to analyze two-photon FLIM images of functionalized gold nanoprobes in Hek293 and human prostate cancer cells. The results further demonstrate that 1D CNNs are fast and can accurately extract lifetime parameters from fluorescence signals. Our study shows that 1D CNNs have great potential in various real-time FLIM applications.

Original languageEnglish
Article number7000210
Number of pages10
JournalIEEE Journal of Selected Topics in Quantum Electronics
Issue number4
Early online date5 Jan 2021
Publication statusPublished - 1 Jul 2021


  • machine-learning
  • fluorescence lifetime imaging
  • convolutional neural networks


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