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

Multistage graph-based segmentation of thoracoscopic images.

Guillaume-Alexandre Bilodeau1, Yueyun Shu, Farida Cheriet

  • 1Ecole Polytechnique de Montréal, C.P.6079, Succ. Centre-ville, Montréal, Que., Canada H3C 3A7. guillaume-alexandre.bilodeau@polymtl.ca

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|October 13, 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

Retinal lesion annotation on fundus imaging: an interobserver variability study.

Scientific reports·2026
Same author

Automatic dental crown generation with spatial constraint modeling.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

DTG: Dual transformers-based generative adversarial networks for retinal 2D/3D OCT image classification.

Medical image analysis·2026
Same author

Automatic margin line extraction using 3D deep learning on digital surface models of prepared teeth for crown generation.

Computers in biology and medicine·2025
Same author

A comprehensive review of ICU readmission prediction models: From statistical methods to deep learning approaches.

Artificial intelligence in medicine·2025
Same author

Personalized dental crown design: A point-to-mesh completion network.

Medical image analysis·2024

This study introduces a novel graph-based segmentation method for thoracoscopic images during scoliosis surgery. The technique enhances image analysis for improved surgical planning and outcomes.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Surgical Technology

Background:

  • Thoracoscopic surgery for scoliosis requires precise visualization of anatomical structures.
  • Accurate segmentation of surgical video is crucial for real-time guidance and post-operative analysis.
  • Existing methods may lack the precision needed for complex spinal procedures.

Purpose of the Study:

  • To develop and evaluate a robust graph-based image segmentation method for thoracoscopic diskectomy procedures.
  • To improve the accuracy and spatial coherence of segmented regions in surgical video.
  • To provide a foundation for enhanced intraoperative guidance in scoliosis treatment.

Main Methods:

  • Pre-processing of thoracoscopic images using Gaussian smoothing, contrast enhancement, and histogram thresholding.

Related Experiment Videos

  • Application of a standard graph-based segmentation approach for initial coarse segmentation.
  • Multistage region merging based on grey-level similarity, region size, and common edge length.

    • Main Results:

    • The proposed method achieves good spatial coherence in segmented regions.
    • Accurate localization of anatomical edges within the thoracoscopic images.
    • Effective segmentation of regions of interest crucial for diskectomy procedures.

    Conclusions:

    • The multistage graph-based segmentation method offers a significant improvement for analyzing thoracoscopic surgical images.
    • This technique enhances the precision of image segmentation, aiding in scoliosis treatment.
    • The approach demonstrates potential for integration into advanced surgical navigation systems.