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

Tagging and Fusion Proteins01:24

Tagging and Fusion Proteins

6.4K
Proteins are involved in several cellular processes and biochemical reactions. Analyzing a specific protein of interest requires it to be isolated from the other proteins in the cell. This is achieved by overexpressing the specific gene in a suitable host to produce large quantities of the target protein. A tag or label is recombined with the gene to produce a fusion protein containing the target protein and the tag. The tags on these fusion proteins can then be used for easy detection and...
6.4K

You might also read

Related Articles

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

Sort by
Same author

LOTUS: Latent Outpainting Diffusion Model for Three-Dimensional Ultrasound Stitching.

Proceedings of machine learning research·2026
Same author

SEGMENTATION CONFIDENCE FOR ARBITRARY CNNS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same author

VFMStitch: A Vision-Foundation-Model Empowered Framework for 3D Ultrasound Stitching via Geometric-Semantic Feature Fusion.

Proceedings of machine learning research·2026
Same author

From Geometry to Intensity: A Coarse-to-Fine Pipeline for Unsupervised 3D Ultrasound Stitching.

Proceedings of SPIE--the International Society for Optical Engineering·2026
Same author

10-Butyl Ether Minocycline (BEM) Reduced Ethanol Consumption in Binge Drinking Mice.

Alcoholism treatment quarterly·2026
Same author

Chromosome-level genomes of scleractinian corals: gene prediction and functional annotation.

Scientific data·2026

Related Experiment Video

Updated: May 1, 2026

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

996

Multi-atlas segmentation with robust label transfer and label fusion.

Hongzhi Wang, Alison Pouch, Manabu Takabe

    Information Processing in Medical Imaging : Proceedings of the ... Conference
    |April 2, 2014
    PubMed
    Summary

    This study introduces improved multi-atlas segmentation for medical imaging by enhancing label transfer and fusion techniques. The methods reduce errors from image registration, improving segmentation accuracy in 3D transesophageal echocardiography (TEE).

    More Related Videos

    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

    3.6K
    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    335

    Related Experiment Videos

    Last Updated: May 1, 2026

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

    Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

    Published on: July 5, 2024

    996
    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

    3.6K
    Automated Joint Space Detection Improves Bone Segmentation Accuracy
    06:45

    Automated Joint Space Detection Improves Bone Segmentation Accuracy

    Published on: November 28, 2025

    335

    Area of Science:

    • Medical Image Analysis
    • Computational Anatomy

    Background:

    • Multi-atlas segmentation is crucial in medical imaging, using registration to transfer labels from atlases to target images.
    • Registration errors can degrade segmentation quality, necessitating robust label fusion methods.

    Purpose of the Study:

    • To enhance registration-based label transfer by generating multiple warped atlases via composed registration paths.
    • To improve label fusion performance against registration errors by integrating probabilistic models with joint label fusion.

    Main Methods:

    • A novel label transfer scheme using composed registration paths and atlas selection guided by segmentations.
    • Integration of a probabilistic correspondence model with joint label fusion for enhanced error reduction.

    Main Results:

    • The proposed label transfer scheme effectively addresses cumulative registration errors through atlas selection.
    • The integrated label fusion technique significantly improves performance against registration inaccuracies.

    Conclusions:

    • The developed techniques enhance the accuracy and robustness of multi-atlas segmentation in medical image analysis.
    • The methods demonstrate effectiveness, particularly for mitral-valve segmentation in 3D transesophageal echocardiography (TEE).