Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Transmission Electron Microscopy

Transmission Electron Microscopy

In 1931, physicist Ernst Ruska—building on the idea that magnetic fields can direct an electron beam just as lenses can direct a beam of light in an optical microscope—developed the first prototype of the electron microscope. This development led to the development of the field of electron microscopy. In the transmission electron microscope (TEM), electrons are produced by a hot tungsten element and accelerated by a potential difference in an electron gun, which gives them up to 400...

Transformers in Distribution System

Transformers in Distribution System

Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Cracking the code of heatstroke: new insights into diagnosis and pathological mechanisms.

International journal of legal medicine·2026

Same author

Westerly‑transported Central Asian biomass burning seasonally pollutes cities in Northwest China.

Journal of hazardous materials·2026

Same author

OpenIO: An open framework for AI-native immunotherapy.

Cancer cell·2026

Same author

A quantitative and precision‑oriented neuronal reconstruction approach based on data grading.

Brain informatics·2026

Same author

A Million-Scale, Difficulty-Stratified Optical-Microscopy Image Annotation for Neuron Reconstruction.

Scientific data·2026

Same author

Unraveling the effector mechanism of citrulline on sow lactation and offspring growth: an integrative multi-omics analysis.

Journal of animal science and biotechnology·2026

Same journal

K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.

Briefings in bioinformatics·2026

Same journal

Accurate prediction of asparagine deamidation in biologics using advanced machine learning models.

Briefings in bioinformatics·2026

Same journal

scImmuneCo: a compendium of cell-type-specific functional modules for decoding immune responses from single-cell RNA-seq data.

Briefings in bioinformatics·2026

Same journal

scGenoByte: a GenoByte embedding transformer with biological priors for cell type annotation.

Briefings in bioinformatics·2026

Same journal

FerroScore: a statistical approach for quantifying tumor-related ferroptosis based on omics data.

Briefings in bioinformatics·2026

Same journal

METEOR: a data-adaptive Mendelian randomization method for powerful detection of shared and specific exposures underlying multiple outcomes.

Briefings in bioinformatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 12, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

VISN: virus instance segmentation network for TEM images using deep attention transformer.

Chi Xiao^1,2, Jun Wang³, Shenrong Yang^1,2

¹State key laboratory of digital medical engineering, School of Biomedical Engineering, Hainan University, 570228, Haikou, China.

Briefings in Bioinformatics

|October 30, 2023

Summary

This summary is machine-generated.

This study introduces an AI-powered method for virus instance segmentation in TEM images, improving the identification of SARS-CoV-2 and other respiratory viruses for expert analysis.

Keywords:

SARS-CoV-2 Transformer deep learning transmission electron microscopy virus instance segmentation

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jul 12, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Virology
Medical Imaging
Artificial Intelligence

Background:

Virus identification from transmission electron microscopy (TEM) images traditionally relies on expert interpretation.
Deep learning advances offer automated virus recognition, but instance segmentation in TEM images remains a challenge.
Existing methods often focus on classification or semantic segmentation, limiting detailed virus identification.

Purpose of the Study:

To develop an effective AI-driven method for virus instance segmentation in TEM images.
To improve the identification and information provided to experts regarding severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and other respiratory viruses.
To create a public dataset and benchmark for virus instance segmentation in TEM.

Main Methods:

Proposed an instance segmentation network utilizing the You Only Look At CoefficienTs (YOLACT) backbone.
Integrated Swin Transformer, dense connections, and a coordinate-spatial attention mechanism for enhanced feature extraction.
Trained and evaluated the network on a newly created public TEM virus dataset, including SARS-CoV-2, H1N1, RSV, HSV-1, AdV5, and Vaccinia virus.

Main Results:

The proposed method achieved a mean average precision (mAP) of 83.8 and an F1 score of 0.920.
Outperformed existing state-of-the-art instance segmentation algorithms on the virus TEM dataset.
Demonstrated high accuracy in segmenting and identifying multiple types of viruses, including SARS-CoV-2.

Conclusions:

The developed automated method offers a powerful tool for virologists in recognizing and identifying viruses from TEM images.
This approach can assist in the diagnosis of viral infections by providing more effective and precise information.
The public dataset and code facilitate further research and development in automated virus identification.