ArticleCommunications biology2022
Generative adversarial network enables rapid and robust fluorescence lifetime image analysis in live cells.
Article in Communications biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- A Chemigenetic Fluorescence Lifetime Biosensor for Dopamine Sensing.bioRxiv : the preprint server for biology · 2026Article
- Article
- Lifetime-based multiplexed detection of viral RNA using fluorogenic aptamers.bioRxiv : the preprint server for biology · 2026Article
- Article
- The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.eLife · 2025Article
- Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging.Journal of the American Chemical Society · 2025Article
- The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.bioRxiv : the preprint server for biology · 2025Article
- Genetically Encoded Fluorogenic DNA Aptamers for Imaging Metabolite in Living Cells.Journal of the American Chemical Society · 2025Article
- Fluorescence Lifetime Endoscopy with a Nanosecond Time-Gated CAPS Camera with IRF-Free Deep Learning Method.Sensors (Basel, Switzerland) · 2025Article
- Overcoming photon and spatiotemporal sparsity in fluorescence lifetime imaging with SparseFLIM.Communications biology · 2024Article
- Assessing the impact of extracellular matrix fiber orientation on breast cancer cellular metabolism.Cancer cell international · 2024Article
- Pioglitazone Phases and Metabolic Effects in Nanoparticle-Treated Cells Analyzed via Rapid Visualization of FLIM Images.Molecules (Basel, Switzerland) · 2024Article
- Two-photon autofluorescence lifetime assay of rabbit photoreceptors and retinal pigment epithelium during light-dark visual cycles in rabbit retina.Biomedical optics express · 2024Article
- 3D convolutional neural networks predict cellular metabolic pathway use from fluorescence lifetime decay data.APL bioengineering · 2024Article
- Applications of machine learning in time-domain fluorescence lifetime imaging: a review.Methods and applications in fluorescence · 2024Review
- Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging.Scientific reports · 2024Article
- Multiplexed imaging in live cells using pulsed interleaved excitation spectral FLIM.Optics express · 2024Article
- Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.PloS one · 2024Article
- Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding.Advanced drug delivery reviews · 2023Review
- Spatial resolution enhancement in photon-starved STED imaging using deep learning-based fluorescence lifetime analysis.Nanoscale · 2023Article
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Authors and funding
12 authors.
Funding
Abstract
Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study molecular states in complex cellular environment as the lifetime readings are not biased by fluorophore concentration or excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is up to 2,800 times faster than the gold standard time-domain maximum likelihood estimation (TD_MLE) and that flimGANE provides a more accurate analysis of low-photon-count histograms in barcode identification, cellular structure visualization, Förster resonance energy transfer characterization, and metabolic state analysis in live cells. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where high-speed analysis is critical.
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