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 Experiment Videos

Comparison of vessel segmentations using staple.

Julien Jomier1, Vincent LeDigarcher, Stephen R Aylward

  • 1Computer-Aided Diagnosis and Display Lab, The University of North Carolina at Chapel Hill, Department of Radiology, 27510 Chapel Hill, USA. jomier@unc.edu

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|May 12, 2006
PubMed
Summary
This summary is machine-generated.

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

A Feature-based Affine Registration Method for Capturing Background Lung Tissue Deformation for Ground Glass Nodule Tracking.

Computer methods in biomechanics and biomedical engineering. Imaging & visualization·2022
Same author

Accuracy and consistency of intensity-based deformable image registration in 4DCT for tumor motion estimation in liver radiotherapy planning.

PloS one·2022
Same author

Perfusion Imaging: An Advection Diffusion Approach.

IEEE transactions on medical imaging·2021
Same author

Automatic Spine Ultrasound Segmentation for Scoliosis Visualization and Measurement.

IEEE transactions on bio-medical engineering·2020
Same author

A new preclinical ultrasound platform for widefield 3D imaging of rodents.

The Review of scientific instruments·2018
Same author

Image-Based Methods for Phase Estimation, Gating, and Temporal Superresolution of Cardiac Ultrasound.

IEEE transactions on bio-medical engineering·2018
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
See all related articles

We developed a new method to validate vascular segmentation accuracy using morphological operators and the TAPLE algorithm. This approach provides ground truth for centerline extraction and can be applied to various open-curve structures.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Biomedical Engineering

Background:

  • Accurate vascular segmentation is crucial for diagnosing and treating various medical conditions.
  • Existing methods for vascular segmentation validation often lack robustness and generalizability.
  • Objective quantitative assessment of segmentation algorithms is essential for clinical translation.

Purpose of the Study:

  • To introduce a novel and extensible method for validating vascular segmentation techniques.
  • To establish a reliable ground truth for centerline extraction in vascular structures.
  • To quantitatively compare the performance of different vascular segmentation algorithms.

Main Methods:

  • The proposed method integrates morphological operators with the TAPLE algorithm for robust centerline extraction.

Related Experiment Videos

  • Ground truth generation for vascular segmentations is achieved through this combined approach.
  • A comparative study was conducted using three distinct vascular segmentation methods: ridge traversal, statistical, and curve level set algorithms.
  • Manual segmentations from five experts served as a benchmark for comparison.

    • Main Results:

    • The novel method successfully generated ground truth for centerline extractions, enabling accurate validation.
    • Quantitative measures of accuracy were obtained for the compared vascular segmentation techniques.
    • The study demonstrated the applicability of the method to open-curve structures beyond vascular networks.

    Conclusions:

    • The proposed validation technique offers a significant advancement in assessing vascular segmentation performance.
    • The method provides a reliable framework for comparing different segmentation algorithms objectively.
    • Its adaptability to various open-curve structures enhances its utility in diverse image analysis applications.