ArticleFrontiers in neuroscience2020
Machine Learning Methods for Fluorescence Lifetime Imaging (FLIM) Based Label-Free Detection of Microglia.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 26 citations in OpenAlex.
- PSF-Driven Spatiotemporal Blending in Fluorescence Lifetime Imaging Microscopy and Its Mitigation via Mean-Shift Super-Resolution-Based Masking.ACS photonics · 2026Article
- Neural Network-Based System for Rapid Diagnostic Decision Support Using FLIM Data: Segmentation and Classification in Liver, Skin, and Pancreatic Tissues.International journal of molecular sciences · 2026Article
- Measuring Metabolic Changes in Cancer Cells Using Two-Photon Fluorescence Lifetime Imaging Microscopy and Machine-Learning Analysis.Journal of biophotonics · 2025Article
- Vibrational spectroscopy and multiphoton microscopy for label-free visualization of nervous system degeneration and regeneration.Biophysical reviews · 2024Review
- Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging.Scientific reports · 2024Article
- Phasor identifier: A cloud-based analysis of phasor-FLIM data on Python notebooks.Biophysical reports · 2023Article
- Advancing Multi-Dimensional Vision: AI-Driven Imaging Using Unique Photodetectors with Integrated Surface Nanostructures.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Unrolled-DOT: an interpretable deep network for diffuse optical tomography.Journal of biomedical optics · 2023Article
- Single cell metabolic imaging of tumor and immune cellsFrontiers in oncology · 2023Article
- Early Detection of Cervical Cancer by Fluorescence Lifetime Imaging Microscopy Combined with Unsupervised Machine Learning.International journal of molecular sciences · 2022Article
- Ex vivo hypercellular parathyroid gland differentiation using dynamic optical contrast imaging (DOCI).Biomedical optics express · 2022Article
- Deep learning in macroscopic diffuse optical imaging.Journal of biomedical optics · 2022Review
- A deep learning framework for classifying microglia activation state using morphology and intrinsic fluorescence lifetime data.Frontiers in neuroinformatics · 2022Article
- Fluorescence lifetime imaging of NAD(P)H upon oxidative stress inFrontiers in bioengineering and biotechnology · 2022Article
- Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors.Sensors (Basel, Switzerland) · 2021Review
- Deep Learning in Biomedical Optics.Lasers in surgery and medicine · 2021Review
- Recent innovations in fluorescence lifetime imaging microscopy for biology and medicine.Journal of biomedical optics · 2021Article
- Phasor-based image segmentation: machine learning clustering techniques.Biomedical optics express · 2021Article
- Joint regression-classification deep learning framework for analyzing fluorescence lifetime images using NADH and FAD.Biomedical optics express · 2021Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Automated computational analysis techniques utilizing machine learning have been demonstrated to be able to extract more data from different imaging modalities compared to traditional analysis techniques. One new approach is to use machine learning techniques to existing multiphoton imaging modalities to better interpret intrinsically fluorescent cellular signals to characterize different cell types. Fluorescence Lifetime Imaging Microscopy (FLIM) is a high-resolution quantitative imaging tool that can detect metabolic cellular signatures based on the lifetime variations of intrinsically fluorescent metabolic co-factors such as nicotinamide adenine dinucleotide [NAD(P)H]. NAD(P)H lifetime-based discrimination techniques have previously been used to develop metabolic cell signatures for diverse cell types including immune cells such as macrophages. However, FLIM could be even more effective in characterizing cell types if machine learning was used to classify cells by utilizing FLIM parameters for classification. Here, we demonstrate the potential for FLIM-based, label-free NAD(P)H imaging to distinguish different cell types using Artificial Neural Network (ANN)-based machine learning. For our biological use case, we used the challenge of differentiating microglia from other glia cell types in the brain. Microglia are the resident macrophages of the brain and spinal cord and play a critical role in maintaining the neural environment and responding to injury. Microglia are challenging to identify as most fluorescent labeling approaches cross-react with other immune cell types, are often insensitive to activation state, and require the use of multiple specialized antibody labels. Furthermore, the use of these extrinsic antibody labels prevents application in
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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.