Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Large language model-guided CAR-T in silico platform for cytokine optimization in liver cancer with low antigen density.

Journal of hematology & oncology·2026
Same author

Suspected iatrogenic oesophageal perforation presenting as right-sided pneumothorax in a preterm neonate: Diagnostic challenges and management.

Tropical doctor·2026
Same author

No effect of acute pain or self-reported chronic pain on working memory in the Sternberg task.

Scientific reports·2026
Same author

Congenital syphilis in neonates: Persistent diagnostic and treatment challenges in resource-limited settings.

Tropical doctor·2026
Same author

Community-based HyPertension Control (CHPC) in Nepal: Cluster randomized implementation trial protocol.

Public health in practice (Oxford, England)·2026
Same author

AMPK Activation by MK-8722 Measured with [<sup>18</sup>F]FDG-PET Imaging in Rodents and Non-Human Primates.

Molecular imaging and biology·2025

Related Experiment Video

Updated: Jun 19, 2026

In Vivo Imaging Systems (IVIS) Detection of a Neuro-Invasive Encephalitic Virus
10:21

In Vivo Imaging Systems (IVIS) Detection of a Neuro-Invasive Encephalitic Virus

Published on: December 2, 2012

CellVision: A deep learning based image analysis platform to accelerate immuno-plaque assay data processing for

Yi Wang1, Michelle N Ngo2, Shubing Wang2

  • 1Analytical Research & Development, MRL, Merck & Co., Inc., Rahway, NJ, USA.

SLAS Discovery : Advancing Life Sciences R & D
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

CellVision, an AI-powered workflow, automates viral plaque counting in high-throughput assays. This deep learning approach enhances accuracy and speed, eliminating manual review for faster vaccine development.

Keywords:
Dengue vaccineHigh-throughput analyticsImmuno-plaque assayMachine learningParticle classification

More Related Videos

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis
07:46

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis

Published on: October 13, 2023

Visualizing Dengue Virus through Alexa Fluor Labeling
09:11

Visualizing Dengue Virus through Alexa Fluor Labeling

Published on: July 9, 2011

Related Experiment Videos

Last Updated: Jun 19, 2026

In Vivo Imaging Systems (IVIS) Detection of a Neuro-Invasive Encephalitic Virus
10:21

In Vivo Imaging Systems (IVIS) Detection of a Neuro-Invasive Encephalitic Virus

Published on: December 2, 2012

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis
07:46

Platform for Quantitative Detection of Endometrial Immune Cells Based on Immunohistochemistry and Digital Image Analysis

Published on: October 13, 2023

Visualizing Dengue Virus through Alexa Fluor Labeling
09:11

Visualizing Dengue Virus through Alexa Fluor Labeling

Published on: July 9, 2011

Area of Science:

  • Computational biology
  • Bioinformatics
  • Machine learning in life sciences

Background:

  • High-throughput analytical assays require accurate image analysis.
  • Manual image review is a bottleneck in automated workflows.
  • AI-powered image analysis offers potential for automation.

Purpose of the Study:

  • To develop an automated workflow for viral plaque counting.
  • To improve accuracy and efficiency in high-throughput assays.
  • To eliminate the need for manual image review.

Main Methods:

  • Developed CellVision, a Python-based deep learning workflow.
  • Utilized convolutional neural networks for pattern recognition.
  • Integrated image processing for plaque identification and separation.

Main Results:

  • CellVision accurately identifies and counts viral plaques.
  • The workflow successfully separates fused plaques and differentiates them from other objects.
  • Achieved high accuracy, eliminating the need for manual review.

Conclusions:

  • CellVision enables fully automated data analysis for high-throughput assays like µPlaque.
  • The workflow supports antiviral vaccine discovery and development.
  • Demonstrated superior performance compared to a commercial tool.