FLIM data analysis based on Laguerre polynomial decomposition and machine-learning

Guo, Shuxia; Silge, Anja; Bae, Hyeonsoo; Tolstik, T.; Meyer, Tobias; Matziolis, Georg; Schmitt, Michael; Popp, Jürgen; Bocklitz, Thomas

doi:10.1117/1.jbo.26.2.022909

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…In these equations, g i and s i are the coordinates along the horizontal (G) and vertical (S) axis, ω is the modulation frequency, I(t) is the TCSPC data at the i th pixel. The deconvolution-based method is another common form of decay trace analysis technique [4,20]. Deconvolution-based methods recover the lifetime decay from the measured fluorescence signal by deconvolution of the system response function (IRF).…”

Section: Super-resolution Microscopymentioning

confidence: 99%

“…The deconvolution-based method is another common form of decay trace analysis technique [4,20]. Deconvolution-based methods recover the lifetime decay from the measured fluorescence signal by deconvolution of the system response function (IRF).…”

Section: Superresolution Microscopymentioning

confidence: 99%

“…Guo et al [20] described a new method where lifetimes and abundances were inverse modeled from decay traces through ML. They trained a random forest (RF) model with within three aspects: First, the RF model works on a pixel basis and thus does not need retraining when spatial dimensions change.…”

Section: For Examples)mentioning

confidence: 99%

“…However, this method s performance depends on spatial information of the FLIM data, which means the model needs more datasets to train. Guo et al [20] described a new method where lifetimes and abundances were inverse modeled from decay traces through ML. They trained a random forest (RF) model with 3000 artificial traces, which was tested with experimental data to estimate lifetimes and abundances.…”

Section: For Examples)mentioning

confidence: 99%

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Review of Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis Using Machine Learning

Adhikari,

Houhou,

Hniopek

et al. 2023

JETA

Self Cite

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Fluorescence lifetime imaging microscopy (FLIM) has emerged as a promising tool for all scientific studies in recent years. However, the utilization of FLIM data requires complex data modeling techniques, such as curve-fitting procedures. These conventional curve-fitting procedures are not only computationally intensive but also time-consuming. To address this limitation, machine learning (ML), particularly deep learning (DL), can be employed. This review aims to focus on the ML and DL methods for FLIM data analysis. Subsequently, ML and DL strategies for evaluating FLIM data are discussed, consisting of preprocessing, data modeling, and inverse modeling. Additionally, the advantages of the reviewed methods are deliberated alongside future implications. Furthermore, several freely available software packages for analyzing the FLIM data are highlighted.

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Section: Super-resolution Microscopymentioning

confidence: 99%

Section: Superresolution Microscopymentioning

confidence: 99%

Section: For Examples)mentioning

confidence: 99%

Section: For Examples)mentioning

confidence: 99%

See 2 more Smart Citations

Review of Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis Using Machine Learning

Adhikari,

Houhou,

Hniopek

et al. 2023

JETA

Self Cite

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show abstract

“…Like the phasor approach, machine learning has been used to achieve fit-free estimates of fluorescence lifetimes from decays on the pixel level, 21 where simulated data were used to compare a machine learning approach to standard fitting algorithms. The root-mean-squared-error, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for the machine learning approach compared to the standard fitting algorithm, including up to three-component analysis.…”

Section: Pixel-level Analysismentioning

confidence: 99%

Recent innovations in fluorescence lifetime imaging microscopy for biology and medicine

et al. 2021

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Significance: Fluorescence lifetime imaging microscopy (FLIM) measures the decay rate of fluorophores, thus providing insights into molecular interactions. FLIM is a powerful molecular imaging technique that is widely used in biology and medicine.Aim: This perspective highlights some of the major advances in FLIM instrumentation, analysis, and biological and clinical applications that we have found impactful over the last year.Approach: Innovations in FLIM instrumentation resulted in faster acquisition speeds, rapid imaging over large fields of view, and integration with complementary modalities such as singlemolecule microscopy or light-sheet microscopy. There were significant developments in FLIM analysis with machine learning approaches to enhance processing speeds, fit-free techniques to analyze images without a priori knowledge, and open-source analysis resources. The advantages and limitations of these recent instrumentation and analysis techniques are summarized. Finally, applications of FLIM in the last year include label-free imaging in biology, ophthalmology, and intraoperative imaging, FLIM of new fluorescent probes, and lifetime-based Förster resonance energy transfer measurements.Conclusions: A large number of high-quality publications over the last year signifies the growing interest in FLIM and ensures continued technological improvements and expanding applications in biomedical research.

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Fluorescence lifetime imaging microscopy

Torrado,

Pannunzio,

Malacrida

et al. 2024

Nat Rev Methods Primers

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FLIM data analysis based on Laguerre polynomial decomposition and machine-learning

Cited by 4 publications

References 27 publications

Review of Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis Using Machine Learning

Review of Fluorescence Lifetime Imaging Microscopy (FLIM) Data Analysis Using Machine Learning

Recent innovations in fluorescence lifetime imaging microscopy for biology and medicine

Fluorescence lifetime imaging microscopy

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