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

Endoscopic Procedures III: Video Capsule Endoscopy01:28

Endoscopic Procedures III: Video Capsule Endoscopy

1.2K
Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...
1.2K
Endoscopic Studies I: Bronchoscopy and Thoracoscopy01:30

Endoscopic Studies I: Bronchoscopy and Thoracoscopy

877
Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
Bronchoscopy
Description
Bronchoscopy is a procedure that involves direct visualization of the larynx, trachea, and bronchi for diagnostic and therapeutic purposes. A flexible fiber optic or rigid bronchoscope is used to carry out the procedure. The fiber-optic bronchoscope is more frequently used due...
877

You might also read

Related Articles

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

Sort by
Same author

Sigmoid Colon Perforation into an Infected Pelvic Lymphocele after Pelvic Lymphadenectomy and Radiotherapy for Recurrent Endometrial Cancer: a Rare Diagnostic and Therapeutic Challenge.

Maedica·2026
Same author

Pattern of lymph node spread in gastric cancer: Western multicenter retrospective study.

BJS open·2026
Same author

Management of iatrogenic esophageal perforations: a systematic review of non-surgical causes.

Surgical endoscopy·2026
Same author

Advances in Non-Alcoholic Fatty Liver Disease: Pathophysiology, Diagnosis, and Emerging Therapies.

Life (Basel, Switzerland)·2026
Same author

Impact of Gut Microbiota on the Clinical Course and Treatment Outcomes of Colorectal Cancer-A Systematic Review.

Medicina (Kaunas, Lithuania)·2026
Same author

Tumor Deposits in Gastric Cancer: A Systematic Review of the Literature With Meta-Analysis on Prevalence and Prognostic Implications.

Annals of surgery open : perspectives of surgical history, education, and clinical approaches·2026

Related Experiment Video

Updated: Mar 19, 2026

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

3.7K

Shot boundary detection in endoscopic surgery videos using a variational Bayesian framework.

Constantinos Loukas1,2, Nikolaos Nikiteas3, Dimitrios Schizas4

  • 1Simulation Center, Laboratory of Medical Physics, Medical School, National and Kapodistrian University of Athens, Athens, Greece. cloukas@med.uoa.gr.

International Journal of Computer Assisted Radiology and Surgery
|June 13, 2016
PubMed
Summary

This study introduces a variational Bayesian Gaussian mixture model (VBGMM) for accurate shot detection in endoscopic surgery videos. The VBGMM method significantly improves content-based video analysis by enhancing precision and recall in identifying surgical procedure segments.

Keywords:
Border detectionShot detectionSurgeryTrackingVariational BayesVideo content analysis

More Related Videos

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

1.2K
Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis
05:31

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

Published on: September 5, 2020

6.4K

Related Experiment Videos

Last Updated: Mar 19, 2026

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

3.7K
Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility
07:46

Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

Published on: August 9, 2024

1.2K
Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis
05:31

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

Published on: September 5, 2020

6.4K

Area of Science:

  • Medical Imaging and Computer Vision
  • Surgical Video Analysis
  • Content-Based Video Retrieval

Background:

  • The increasing volume of surgical video data necessitates efficient content management and analysis.
  • Current methods for surgical video analysis, particularly shot detection, are still developing.

Purpose of the Study:

  • To develop and evaluate a novel method for shot detection in endoscopic surgery videos.
  • To improve the fundamental step of content-based video analysis for surgical procedures.

Main Methods:

  • Video decomposition into short clips followed by feature extraction.
  • Application of spatiotemporal Gaussian mixture models (GMM) with a variational Bayesian (VB) algorithm for parameter approximation.
  • Automatic determination of the number of GMM components using the VBGMM algorithm and component tracking via Kullback-Leibler divergence for shot border identification.

Main Results:

  • The proposed VBGMM method achieved high performance in shot detection on laparoscopic videos, with precision and recall exceeding 80% and coverage at 84%.
  • Comparison with existing GMM and MotionDiff methods showed superior performance for VBGMM across most metrics.
  • The overflow metric for VBGMM (37%) was higher than the MotionDiff method (27%).

Conclusions:

  • Spatiotemporal modeling using VBGMMs shows promise for accurate shot border detection in surgical videos.
  • The VBGMM approach offers potential for further applications in surgical video analysis, such as component tracking.