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

Masking and Demasking Agents01:19

Masking and Demasking Agents

EDTA titrations may necessitate masking and demasking agents to temporarily protect a particular metal ion in a mixture from the EDTA reaction. These agents facilitate the sequential analysis of the metal ions by forming stable complexes with some—but not all—metal ions during certain steps.
There are many masking agents, such as cyanide, fluoride, triethanolamine, thiourea, and 2,3-bis(sulfanyl)propan-1-ol (formerly 2,3-dimercapto-1-propanol), with the masking agent chosen based on the metal...

You might also read

Related Articles

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

Sort by
Same author

Assessing genotype by feed interactions for milk production traits in dairy cattle.

Journal of dairy science·2026
Same author

Temporal dynamics of bromoform metabolite formation and microbial responses during in vitro rumen fermentation with Asparagopsis taxiformis.

Animal : an international journal of animal bioscience·2026
Same author

Don't let milk output go extinct: postgrazing sward height shapes dairy cow foraging efficiency.

Animal : an international journal of animal bioscience·2026
Same author

Associations between digestibility, feed efficiency, nitrogen efficiency, and nitrogen partitioning in dairy cows on Dutch commercial farms.

Journal of dairy science·2026
Same author

Integration of factors controlling volatile fatty acid stoichiometry, hydrogen dynamics, and methanogenesis in the rumen of dairy cattle: Model description and evaluation.

Journal of dairy science·2026
Same author

Chicory reduces enteric methane emissions and maintains milk yield but decreases milk fat content compared with perennial ryegrass in dairy cows.

Journal of dairy science·2026
Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026
Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026
Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026
Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026
Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

User-agent cooperation in multiagent IVUS image segmentation.

E G P Bovenkamp1, J Dijkstra, J G Bosch

  • 1Division of Image Processing, Departmentof Radiology, Leiden University Medical Center, 2300RC Leiden, TheNetherlands. ernst.bovenkamp@tno.nl

IEEE Transactions on Medical Imaging
|January 1, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a multiagent system for automated image interpretation, reducing variability with simple, high-level user interactions. Expert input minimizes observer differences in complex image analysis, achieving expert-level accuracy.

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jun 26, 2026

Automated Analysis of C. elegans Fluorescence Images using SegElegans
06:27

Automated Analysis of C. elegans Fluorescence Images using SegElegans

Published on: October 10, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Automated interpretation of complex medical images often requires expert knowledge and model-based analysis.
  • High inter- and intra-observer variability can affect the reliability of image interpretation.
  • Existing methods may require complex user interactions for image segmentation refinement.

Purpose of the Study:

  • To develop and evaluate a multiagent image interpretation system with a restricted vocabulary of high-level user interactions.
  • To minimize inter- and intra-observer variability in image analysis by simplifying user input.
  • To improve the accuracy and repeatability of image segmentation through intelligent agent-user collaboration.

Main Methods:

  • A multiagent system with knowledge-based control over image segmentation algorithms was developed.
  • The system utilizes contextual knowledge to minimize user interactions.
  • Users interact at a high level of abstraction, correcting or confirming agent interpretations without needing knowledge of underlying algorithms.
  • The system was applied to intravascular ultrasound (IVUS) images.

Main Results:

  • Substantial improvements in segmentation results were achieved with an average of 2-3 high-level user interactions per correction.
  • User-guided segmentation significantly reduced variability compared to traditional methods.
  • The system demonstrated accuracy and repeatability equivalent to manual segmentation by an expert.

Conclusions:

  • High-level user interaction with a multiagent system effectively reduces observer variability in complex image interpretation.
  • This approach simplifies the refinement process, making expert-level image segmentation more accessible.
  • The developed system shows promise for enhancing the reliability and efficiency of medical image analysis, particularly for intravascular ultrasound (IVUS) imaging.