ArticleScientific reports2021
A deep learning approach to identifying immunogold particles in electron microscopy images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 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.
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
13 citing papers in PubMed, 27 citations in OpenAlex.
- Immunoelectron microscopy: a comprehensive guide from sample preparation to high-resolution imaging.Discover nano · 2025Review
- Physically grounded deep learning-enabled gold nanoparticle localization and quantification in photonic resonator absorption microscopy for digital resolution molecular diagnostics.Biosensors & bioelectronics · 2025Article
- Advances in multimodal imaging techniques in nanomedicine: enhancing drug delivery precision.RSC advances · 2025Review
- Article
- Gold Particle Analyser: Detection and quantitative assessment of electron microscopy gold probes.PloS one · 2023Article
- Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization.Journal of computer-aided molecular design · 2022Article
- Nanoscale defect evaluation framework combining real-time transmission electron microscopy and integrated machine learning-particle filter estimation.Scientific reports · 2022Article
- GAN-Based Video Denoising with Attention Mechanism for Field-Applicable Pig Detection System.Sensors (Basel, Switzerland) · 2022Article
- Image Processing Algorithm for In Situ Monitoring Fiber Laser Remote Cutting by a High-Speed Camera.Sensors (Basel, Switzerland) · 2022Article
- Simple Code Implementation for Deep Learning-Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography.Translational vision science & technology · 2022Article
- Gold In-and-Out: A Toolkit for Analyzing Subcellular Distribution of Immunogold-Labeled Membrane Proteins in Freeze-Fracture Replica Images.Frontiers in neuroanatomy · 2022Article
- Nanomedicine Ex Machina: Between Model-Informed Development and Artificial Intelligence.Frontiers in digital health · 2022Review
- Review of Post-embedding Immunogold Methods for the Study of Neuronal Structures.Frontiers in neuroanatomy · 2021Review
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain conclusive data sets. To automate this process, we developed Gold Digger, a software tool that uses a modified pix2pix deep learning network capable of detecting and annotating colloidal gold particles in biological EM images obtained from both freeze-fracture replicas and plastic sections prepared with the post-embedding method. Gold Digger performs at near-human-level accuracy, can handle large images, and includes a user-friendly tool with a graphical interface for proof reading outputs by users. Manual error correction also helps for continued re-training of the network to improve annotation accuracy over time. Gold Digger thus enables rapid high-throughput analysis of immunogold-labeled EM data and is freely available to the research community.
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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.