Fast analysis of time‐domain fluorescence lifetime imaging via extreme learning machine

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We present a fast and accurate analytical method for fluorescence lifetime imaging microscopy (FLIM), using the extreme learning machine (ELM). We used extensive metrics to evaluate ELM and existing algorithms. First, we compared these algorithms using synthetic datasets. The results indicate that ELM can obtain higher fidelity, even in low-photon conditions. Afterwards, we used ELM to retrieve lifetime components from human prostate cancer cells loaded with gold nanosensors, showing that ELM also outperforms the iterative fitting and non-fitting algorithms. By comparing ELM with a computational efficient neural network, ELM achieves comparable accuracy with less training and inference time. As there is no back-propagation process for ELM during the training phase, the training speed is much higher than existing neural network approaches. The proposed strategy is promising for edge computing with online training.

Original languageEnglish
Article number3758
Number of pages14
Issue number10
Publication statusPublished - 15 May 2022


  • fluorescence lifetime imaging microscopy
  • single‐photon time‐correlated counting (TCSPC)
  • computational imaging
  • machine learning


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