ArticleBiomedical optics express2020
UNMIX-ME: spectral and lifetime fluorescence unmixing via deep learning.
Article in Biomedical optics express, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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
25 citing papers in PubMed, 34 citations in OpenAlex.
- Deep unfolding blind source unmixing for multicolor fluorescence imaging.Biomedical optics express · 2026Article
- Neural Network-Based Depth Retrieval of Fluorescent Targets From Wide-Field Fluorescence Intensity Images in Scattering Tissues.Journal of biophotonics · 2026Article
- Photoacoustic Fingerprinting for Robust Molecular Imaging.bioRxiv : the preprint server for biology · 2026Article
- Article
- Custom-designed Verasonics-based multi-wavelength photoacoustic and ultrasound imaging: from technical advances to preclinical applications in cancer.Biomedical optics express · 2025Review
- Phasor and neural network approaches for rapid fluorophore fraction analysis in temporal-spectral multiplexed data.Journal of biomedical optics · 2025Article
- Live-cell imaging powered by computation.Nature reviews. Molecular cell biology · 2024Review
- BINGO: a blind unmixing algorithm for ultra-multiplexing fluorescence images.Bioinformatics (Oxford, England) · 2024Article
- 5-ALA induced PpIX fluorescence spectroscopy in neurosurgery: a review.Frontiers in neuroscience · 2024Review
- Deep learning assisted classification of spectral photoacoustic imaging of carotid plaques.Photoacoustics · 2023Article
- AutoUnmix: an autoencoder-based spectral unmixing method for multi-color fluorescence microscopy imaging.Biomedical optics express · 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
- Two-Photon Imaging for Non-Invasive Corneal Examination.Sensors (Basel, Switzerland) · 2022Review
- Computational macroscopic lifetime imaging and concentration unmixing of autofluorescence.Journal of biophotonics · 2022Article
- Suppression of natural lens fluorescence in fundus autofluorescence measurements: review of hardware solutions.Biomedical optics express · 2022Review
- Article
- Live-cell fluorescence spectral imaging as a data science challenge.Biophysical reviews · 2022Review
- Deep learning in macroscopic diffuse optical imaging.Journal of biomedical optics · 2022Review
- 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
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Hyperspectral fluorescence lifetime imaging allows for the simultaneous acquisition of spectrally resolved temporal fluorescence emission decays. In turn, the acquired rich multidimensional data set enables simultaneous imaging of multiple fluorescent species for a comprehensive molecular assessment of biotissues. However, to enable quantitative imaging, inherent spectral overlap between the considered fluorescent probes and potential bleed-through must be considered. Such a task is performed via either spectral or lifetime unmixing, typically independently. Herein, we present "UNMIX-ME" (unmix multiple emissions), a deep learning-based fluorescence unmixing routine, capable of quantitative fluorophore unmixing by simultaneously using both spectral and temporal signatures. UNMIX-ME was trained and validated using an
Identifiers
What OpenQuestion holds
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.