ArticleProceedings of the National Academy of Sciences of the United States of America2019
Fast fit-free analysis of fluorescence lifetime imaging via deep learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 85 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
85 citing papers in PubMed, 168 citations in OpenAlex.
- Emerging trends of fluorescence lifetime imaging microscopy (FLIM): advances, challenges, and prospects.Biophysics reports · 2026Article
- High-fidelity fast fluorescence lifetime imaging by event-based denoising.Nature biotechnology · 2026Article
- Segmentation-guided photon pooling enables robust single-cell analysis and fast fluorescence lifetime imaging microscopy.Journal of biomedical optics · 2026Article
- Whole-body tomographic fluorescence lifetime imaging of PD-L1 expression.Research square · 2026Article
- Time-of-flight fluorescence depth mapping using a spatiotemporal deep learning model.Journal of biomedical optics · 2026Article
- Defining the optimal imaging time point for fluorescence-lifetime-based tumor identification using the non-targeting near-infrared dye indocyanine green and post-processed high-dynamic-range images.Biomedical optics express · 2026Article
- Enhancing Fluorescence Lifetime Imaging With Differential Transformer.Journal of biophotonics · 2026Article
- Article
- Fluorescence Lifetime Microscopy Methods for Studying Dynamics of Fluorescent Proteins In Vivo and In Vitro.Advances in experimental medicine and biology · 2026Review
- Fluorescence Lifetime Imaging Techniques-A Review on Principles, Applications and Clinical Relevance.Journal of biophotonics · 2025Review
- Segmentation-guided photon pooling enables robust single cell analysis and fast fluorescence lifetime imaging microscopy.bioRxiv : the preprint server for biology · 2025Article
- Article
- The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.eLife · 2025Article
- Quantifying uncertainty in phasor-based time-domain fluorescence lifetime imaging microscopy.Biomedical optics express · 2025Article
- Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging.Journal of the American Chemical Society · 2025Article
- High-speed fluorescence lifetime imaging microscopy: techniques, applications, and prospects.Biophotonics discovery · 2025Review
- Identification of colorectal malignancies enabled by phasor-based autofluorescence lifetime macroimaging and ensemble learning.Biophotonics discovery · 2025Article
- AlliGator: Open Source Fluorescence Lifetime Imaging Analysis in G.bioRxiv : the preprint server for biology · 2025Article
- Article
- The promise and peril of comparing fluorescence lifetime in biology revealed by simulations.bioRxiv : the preprint server for biology · 2025Article
25 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI image formation that is based on deep learning (DL) to quantify fluorescence decays simultaneously over a whole image and at fast speeds. We report on a deep neural network (DNN) architecture, named fluorescence lifetime imaging network (FLI-Net) that is designed and trained for different classes of experiments, including visible FLI and near-infrared (NIR) FLI microscopy (FLIM) and NIR gated macroscopy FLI (MFLI). FLI-Net outputs quantitatively the spatially resolved lifetime-based parameters that are typically employed in the field. We validate the utility of the FLI-Net framework by performing quantitative microscopic and preclinical lifetime-based studies across the visible and NIR spectra, as well as across the 2 main data acquisition technologies. These results demonstrate that FLI-Net is well suited to accurately quantify complex fluorescence lifetimes in cells and, in real time, in intact animals without any parameter settings. Hence, FLI-Net paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications ranging from fundamental discoveries in molecular and cellular biology to clinical translation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.