ArticleBioinformatics (Oxford, England)2020
Semi-blind sparse affine spectral unmixing of autofluorescence-contaminated micrographs.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- A workflow to reduce red blood cell autofluorescence for imaging IL-4/IL-4R interactions in the inflamed lung.Biochemistry and biophysics reports · 2026Article
- Deep unfolding blind source unmixing for multicolor fluorescence imaging.Biomedical optics express · 2026Article
- Hyperspectral imaging system for multiplexed cancer marker detection: design and clinical evaluation.Journal of biomedical optics · 2026Article
- Indocyanine green excitation-emission matrix characterization: excitation-dependent emission shifts and application-specific spectra.Journal of biomedical optics · 2026Article
- Papanicolaou stain unmixing for RGB image using weighted nucleus sparsity and total variation regularization.Medical & biological engineering & computing · 2026Article
- Cross hybridization Inference for Phylogenetic Resolution (CIPHR)-FISH enables microbiome imaging with strain level taxonomic resolution.bioRxiv : the preprint server for biology · 2026Article
- BEEP Learning: Multi-View Image Decomposition for Massively Multiplexed Biological Fluorescence Microscopy.bioRxiv : the preprint server for biology · 2026Article
- A framework of multi-view machine learning for biological spectral unmixing of fluorophores with overlapping excitation and emission spectra.Briefings in bioinformatics · 2024Article
- A Framework of Multi-View Machine Learning for Biological Spectral Unmixing of Fluorophores with Overlapping Excitation and Emission Spectra.bioRxiv : the preprint server for biology · 2024Article
- BINGO: a blind unmixing algorithm for ultra-multiplexing fluorescence images.Bioinformatics (Oxford, England) · 2024Article
- Comparing Performance of Spectral Image Analysis Approaches for Detection of Cellular Signals in Time-Lapse Hyperspectral Imaging Fluorescence Excitation-Scanning Microscopy.Bioengineering (Basel, Switzerland) · 2023Article
- Unmixing biological fluorescence image data with sparse and low-rank Poisson regression.Bioinformatics (Oxford, England) · 2023Article
- Unmixing Biological Fluorescence Image Data with Sparse and Low-Rank Poisson Regression.bioRxiv : the preprint server for biology · 2023Article
- Development of Spectral Imaging Cytometry.Methods in molecular biology (Clifton, N.J.) · 2023Review
- Live-cell fluorescence spectral imaging as a data science challenge.Biophysical reviews · 2022Review
- Visualizing the Invisible: Advanced Optical Microscopy as a Tool to Measure Biomechanical Forces.Frontiers in cell and developmental biology · 2021Review
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Authors and funding
5 authors.
Funding
Abstract
motivationSpectral unmixing methods attempt to determine the concentrations of different fluorophores present at each pixel location in an image by analyzing a set of measured emission spectra. Unmixing algorithms have shown great promise for applications where samples contain many fluorescent labels; however, existing methods perform poorly when confronted with autofluorescence-contaminated images.
resultsWe propose an unmixing algorithm designed to separate fluorophores with overlapping emission spectra from contamination by autofluorescence and background fluorescence. First, we formally define a generalization of the linear mixing model, called the affine mixture model (AMM), that specifically accounts for background fluorescence. Second, we use the AMM to derive an affine nonnegative matrix factorization method for estimating fluorophore endmember spectra from reference images. Lastly, we propose a semi-blind sparse affine spectral unmixing (SSASU) algorithm that uses knowledge of the estimated endmembers to learn the autofluorescence and background fluorescence spectra on a per-image basis. When unmixing real-world spectral images contaminated by autofluorescence, SSASU greatly improved proportion indeterminacy as compared to existing methods for a given relative reconstruction error. AVAILABILITY AND IMPLEMENTATION: The source code used for this paper was written in Julia and is available with the test data at https://github.com/brossetti/ssasu.
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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.