ArticleProceedings of the National Academy of Sciences of the United States of America2020
Accurate and rapid background estimation in single-molecule localization microscopy using the deep neural network BGnet.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
39 citing papers in PubMed.
- Fused deep learning enables 6D single-molecule localization in polarization-resolved microscopy.Methods and applications in fluorescence · 2026Article
- Advancement of Peptide-PAINT: Applications and Current Challenges in Super-Resolution Imaging.Small science · 2026Review
- Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2026Article
- Digital-micromirror-device-based illumination strategies for background suppression in single-molecule localization microscopy.Biomedical optics express · 2026Article
- Far-field single-molecule vibrational spectroscopy and imaging.Chemical science · 2026Review
- Functional super-resolution microscopy of fibers and polymers: convergence of artificial and biological systems at the nanoscale.Nanoscale horizons · 2026Review
- Deep Learning-Based Event Classification of Mass Photometry Data for Optimal Mass Measurement at the Single-Molecule Level.ACS nano · 2026Article
- 3D localization of retrovirus assembly in the presence of structured background with deep learning.Biophysical journal · 2025Article
- Enhancing ultra-high density single-molecule localization with deep spatiotemporal networks.Biomedical optics express · 2025Article
- Optimized molecule detection in localization microscopy with selected false positive probability.Nature communications · 2025Article
- Comprehensive translational profiling and STE AI uncover rapid control of protein biosynthesis during cell stress.Nucleic acids research · 2024Article
- Live-cell imaging powered by computation.Nature reviews. Molecular cell biology · 2024Review
- Deep learning-based spectroscopic single-molecule localization microscopy.Journal of biomedical optics · 2024Article
- RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds.The journal of physical chemistry. B · 2024Article
- Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.Optics express · 2024Article
- Combining deep learning approaches and point spread function engineering for simultaneous 3D position and 3D orientation measurements of fluorescent single molecules.Optics communications · 2023Article
- Switchable and Functional Fluorophores for Multidimensional Single-Molecule Localization Microscopy.Chemical & biomedical imaging · 2023Review
- Quantitative Single-Molecule Localization Microscopy.Annual review of biophysics · 2023Review
- Deep learning using a residual deconvolutional network enables real-time high-density single-molecule localization microscopy.Biomedical optics express · 2023Article
- Machine-learning-powered extraction of molecular diffusivity from single-molecule images for super-resolution mapping.Communications biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Background fluorescence, especially when it exhibits undesired spatial features, is a primary factor for reduced image quality in optical microscopy. Structured background is particularly detrimental when analyzing single-molecule images for 3-dimensional localization microscopy or single-molecule tracking. Here, we introduce BGnet, a deep neural network with a U-net-type architecture, as a general method to rapidly estimate the background underlying the image of a point source with excellent accuracy, even when point-spread function (PSF) engineering is in use to create complex PSF shapes. We trained BGnet to extract the background from images of various PSFs and show that the identification is accurate for a wide range of different interfering background structures constructed from many spatial frequencies. Furthermore, we demonstrate that the obtained background-corrected PSF images, for both simulated and experimental data, lead to a substantial improvement in localization precision. Finally, we verify that structured background estimation with BGnet results in higher quality of superresolution reconstructions of biological structures.
Indexed as
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.