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

Point-based geometric deformable models for medical image segmentation.

Hon Pong Ho1, Yunmei Chen, Huafeng Liu

  • 1Dept. of EEE, Hong Kong University of Science & Technology, Hong Kong. garyho@ust.hk

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

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

IEEE transactions on medical imaging·2026
Same author

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same author

On the Identifiability of Hybrid Deep Generative Models: Meta-Learning as a Solution.

Advances in neural information processing systems·2026
Same author

Mitochondrial quality control in health and disease: Updates 2026.

Chinese medical journal·2026
Same author

PD-1 H (VISTA) drives immunosuppressive reprogramming of glioma-associated myeloid cells to promote glioma progression.

Journal of translational medicine·2026
Same author

Seeing is believing: visualizing spatiotemporal lipophagic flux in vivo with the tfLiveDrop reporter mice.

Autophagy reports·2026
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

This study introduces a novel point-based approach for level set image segmentation, eliminating the need for complex grid structures. This method enhances efficiency and precision in medical image analysis, particularly for brain structure segmentation.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Computational Geometry

Background:

  • Traditional level set image segmentation relies on grid/mesh structures, posing challenges for adaptive refinement and computational cost.
  • Grid-based methods require expensive and difficult grid refinement or remeshing for optimizing time and precision trade-offs.

Purpose of the Study:

  • To present a novel point-based level set evolution method that avoids computational grids.
  • To enable adaptive sampling and density determination based on level set geometry and image data.
  • To implement and evaluate general geometric deformable models in this new framework.

Main Methods:

  • Developed a point-based representation for level set evolution, removing the need for explicit spatial discretization.
  • Integrated region-based prior information into domain sampling and curve evolution processes.

Related Experiment Videos

  • Applied the method to segment brain structures from 3D magnetic resonance images and evaluated on synthetic data.

    • Main Results:

    • Successfully implemented general geometric deformable models in a grid-free, point-based environment.
    • Demonstrated adaptive sampling and density control driven by level set geometry and image information.
    • Achieved accurate surface segmentation of brain structures from 3D MRI data.

    Conclusions:

    • The proposed point-based level set method offers a grid-free alternative for image segmentation, overcoming limitations of traditional approaches.
    • This approach facilitates adaptive domain sampling and density, improving efficiency and precision.
    • The method shows promise for medical image analysis, particularly in segmenting complex structures like those in 3D MRI scans.