ArticleProceedings of the National Academy of Sciences of the United States of America2018
Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 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.
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Who cites it
64 citing papers in PubMed.
- Machine Learning-Assisted Quantification of Organelle Abundance.Bio-protocol · 2026Article
- MicroLive: an image processing toolkit for quantifying live-cell single-molecule microscopy.Bioinformatics advances · 2026Article
- D‑Blur: A Deep Learning Approach for Mapping Subdiffraction Diffusion with Motion-Blurred Images.Chemical & biomedical imaging · 2025Article
- Engineering Decavalent, Sperm-Binding Laminin-IgG Hybrid Antibodies for Potent Non-Hormonal Contraception.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Deep learning detection of dynamic exocytosis events in fluorescence TIRF microscopy.PLoS computational biology · 2025Article
- MicroLive: An Image Processing Toolkit for Quantifying Live-cell Single-Molecule Microscopy.bioRxiv : the preprint server for biology · 2025Article
- Single camera estimation of microswimmer depth with a convolutional network.Journal of the Royal Society, Interface · 2025Article
- From Neural Networks to Transformers: Achieving High and Fast Precision in Fluorescence Correlation Spectroscopy Analysis.ACS omega · 2025Article
- The challenge of measuring mosquito flight performance: going beyond sterile insect technique and into transgenic and gene drive-based approaches.Open biology · 2025Review
- Nellie: automated organelle segmentation, tracking and hierarchical feature extraction in 2D/3D live-cell microscopy.Nature methods · 2025Article
- MSP-tracker: A versatile vesicle tracking software tool used to reveal the spatial control of polarized secretion in Drosophila epithelial cells.PLoS biology · 2025Article
- DeepKymoTracker: A tool for accurate construction of cell lineage trees for highly motile cells.PloS one · 2025Article
- ZMapp reduces diffusion of Ebola viral particles in fresh human cervicovaginal mucus.Emerging microbes & infections · 2024Article
- Identifying and training deep learning neural networks on biomedical-related datasets.Briefings in bioinformatics · 2024Article
- Deep learning for optical tweezers.Nanophotonics (Berlin, Germany) · 2024Review
- Bellybutton: accessible and customizable deep-learning image segmentation.Scientific reports · 2024Article
- Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods.Micromachines · 2024Review
- Inhaled "Muco-Trapping" Monoclonal Antibody Effectively Treats Established Respiratory Syncytial Virus (RSV) Infections.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Deep learning-based size prediction for optical trapped nanoparticles and extracellular vesicles from limited bandwidth camera detection.Biomedical optics express · 2024Article
- Polarization differential interference contrast microscopy with physics-inspired plug-and-play denoiser for single-shot high-performance quantitative phase imaging.Biomedical optics express · 2023Article
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Particle tracking is a powerful biophysical tool that requires conversion of large video files into position time series, i.e., traces of the species of interest for data analysis. Current tracking methods, based on a limited set of input parameters to identify bright objects, are ill-equipped to handle the spectrum of spatiotemporal heterogeneity and poor signal-to-noise ratios typically presented by submicron species in complex biological environments. Extensive user involvement is frequently necessary to optimize and execute tracking methods, which is not only inefficient but introduces user bias. To develop a fully automated tracking method, we developed a convolutional neural network for particle localization from image data, comprising over 6,000 parameters, and used machine learning techniques to train the network on a diverse portfolio of video conditions. The neural network tracker provides unprecedented automation and accuracy, with exceptionally low false positive and false negative rates on both 2D and 3D simulated videos and 2D experimental videos of difficult-to-track species.
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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.