Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine
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 E...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/10/3758 |
| _version_ | 1797495776987840512 |
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| author | Zhenya Zang Dong Xiao Quan Wang Zinuo Li Wujun Xie Yu Chen David Day Uei Li |
| author_facet | Zhenya Zang Dong Xiao Quan Wang Zinuo Li Wujun Xie Yu Chen David Day Uei Li |
| author_sort | Zhenya Zang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T01:54:29Z |
| format | Article |
| id | doaj.art-e4a89f07fd4f42da95765e0795e26303 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T01:54:29Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-e4a89f07fd4f42da95765e0795e263032023-11-23T13:00:41ZengMDPI AGSensors1424-82202022-05-012210375810.3390/s22103758Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning MachineZhenya Zang0Dong Xiao1Quan Wang2Zinuo Li3Wujun Xie4Yu Chen5David Day Uei Li6Department of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UKDepartment of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UKDepartment of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UKDepartment of Physics, University of Strathclyde, Glasgow G4 0NG, UKDepartment of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UKDepartment of Physics, University of Strathclyde, Glasgow G4 0NG, UKDepartment of Biomedical Engineering, University of Strathclyde, Glasgow G4 0RE, UKWe 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.https://www.mdpi.com/1424-8220/22/10/3758fluorescence lifetime imaging microscopysingle-photon time-correlated counting (TCSPC)computational imagingmachine learning |
| spellingShingle | Zhenya Zang Dong Xiao Quan Wang Zinuo Li Wujun Xie Yu Chen David Day Uei Li Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine Sensors fluorescence lifetime imaging microscopy single-photon time-correlated counting (TCSPC) computational imaging machine learning |
| title | Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine |
| title_full | Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine |
| title_fullStr | Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine |
| title_full_unstemmed | Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine |
| title_short | Fast Analysis of Time-Domain Fluorescence Lifetime Imaging via Extreme Learning Machine |
| title_sort | fast analysis of time domain fluorescence lifetime imaging via extreme learning machine |
| topic | fluorescence lifetime imaging microscopy single-photon time-correlated counting (TCSPC) computational imaging machine learning |
| url | https://www.mdpi.com/1424-8220/22/10/3758 |
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