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

You might also read

Related Articles

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

Sort by
Same author

Integration of Wearable Technology into Vascular Surgery: Insights from an International Online Cross-Sectional Survey.

Annals of vascular surgery·2026
Same author

French Nationwide Outcomes after Revascularisation for Acute and Chronic Mesenteric Ischaemia.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery·2026
Same author

One-Year Outcomes of the First 1000 Patients Implanted With the Medtronic Micra AV Leadless Pacing System in France: The AV-CESAR Cohort Study.

Circulation. Arrhythmia and electrophysiology·2026
Same author

A Core Outcome Set for Intact Abdominal Aortic Aneurysms Under Surveillance.

Annals of vascular surgery·2026
Same author

Managing Adhesive Small Bowel Obstruction: Immediate Risks and Long-Term Burden in France.

Annals of surgery·2026
Same author

French National Analysis of Outcomes of Patients with Lower Limb Arterial Aneurysms Who Underwent Vascular Intervention.

Annals of vascular surgery·2026

Related Experiment Video

Updated: Jan 19, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.7K

A fully automated pipeline for mining abdominal aortic aneurysm using image segmentation.

Fabien Lareyre1,2, Cédric Adam3, Marion Carrier3

  • 1Cardiovascular Surgery Unit, Cardio Thoracic Centre of Monaco, Monaco, Monaco. fabien.lareyre@gmail.com.

Scientific Reports
|September 26, 2019
PubMed
Summary

A new automated imaging software accurately detects abdominal aortic aneurysms (AAA) and their characteristics from CT scans. This tool enhances diagnosis and treatment planning for AAA disease.

More Related Videos

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

737

Related Experiment Videos

Last Updated: Jan 19, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.7K
Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions
06:18

Author Spotlight: Segmentation and VR for Advanced Neurovascular Interventions

Published on: April 5, 2024

1.5K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

737

Area of Science:

  • Medical Imaging
  • Cardiovascular Surgery
  • Radiology

Background:

  • Abdominal aortic aneurysms (AAA) require precise imaging for diagnosis and treatment.
  • Current imaging analysis can be time-consuming and may lack full automation.

Purpose of the Study:

  • To develop a fully automated software system for rapid and reliable detection of the vascular system and AAA.
  • To enable detailed analysis of AAA characteristics, including thrombus and calcifications.

Main Methods:

  • Utilized a dataset of CT scans from 40 patients with AAA.
  • Applied image filtering for noise reduction and border propagation for aortic lumen localization.
  • Implemented online error detection and morphological snakes for 2D/3D segmentation.

Main Results:

  • The software successfully performed automated detection of the aortic lumen and AAA features.
  • Identified thrombus and calcifications within the aneurysms.
  • Provided 2D and 3D reconstructions for visualization and evaluation.

Conclusions:

  • The developed software offers a fast, automated solution for analyzing AAA anatomy.
  • This tool has potential utility in clinical practice and large-scale research for AAA management.