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

Periductal iron-corrected T1 is a predictor of adverse outcomes in large-duct primary sclerosing cholangitis.

BMC medical imaging·2026
Same author

AI portal tract detection and characterisation for a regional analysis of steatosis and inflammation in MASLD, MASH and AIH.

Journal of clinical pathology·2025
Same author

Standardized pancreatic MRI-T1 measurement methods: comparison between manual measurement and a semi-automated pipeline with automatic quality control.

The British journal of radiology·2025
Same author

Quantitative digital pathology enables automated and quantitative assessment of inflammatory activity in patients with autoimmune hepatitis.

Journal of pathology informatics·2024
Same author

Estimation of field inhomogeneity map following magnitude-based ambiguity-resolved water-fat separation.

Magnetic resonance imaging·2023
Same author

Pancreas MRI Segmentation Into Head, Body, and Tail Enables Regional Quantitative Analysis of Heterogeneous Disease.

Journal of magnetic resonance imaging : JMRI·2022

Related Experiment Video

Updated: Sep 13, 2025

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

2.9K

Automatic Couinaud segmentation using AI and pictorial representation landmarking.

Luis Miguel Núñez1, Paul Aljabar2, Sir Michael Brady2

  • 1Perspectum Ltd, Oxford, United Kingdom. luis.nunez@perspectum.com.

Abdominal Radiology (New York)
|July 30, 2025
PubMed
Summary

A new framework improves liver surgery planning by accurately segmenting Couinaud segments using deep learning and landmark identification. This method enhances precision and streamlines workflows for better patient outcomes.

More Related Videos

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.3K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

Related Experiment Videos

Last Updated: Sep 13, 2025

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

2.9K
Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease
08:41

Novel In Vivo Micro-Computed Tomography Imaging Techniques for Assessing the Progression of Non-Alcoholic Fatty Liver Disease

Published on: March 24, 2023

1.3K
Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
12:50

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

Published on: April 14, 2014

40.3K

Area of Science:

  • Medical Imaging
  • Surgical Planning
  • Artificial Intelligence in Medicine

Background:

  • Accurate delineation of Couinaud segments is crucial for liver surgery and monitoring.
  • Traditional methods for segment delineation are labor-intensive and prone to variability.
  • Existing automated methods often lack accuracy or require extensive post-processing.

Purpose of the Study:

  • To develop and evaluate a novel framework for precise Couinaud segment localization and volume estimation.
  • To integrate deep learning-based segmentation with landmark identification for personalized liver models.
  • To improve the accuracy and efficiency of liver anatomy assessment for surgical applications.

Main Methods:

  • A framework combining deep learning segmentation and auxiliary landmark identification was developed.
  • A personalized pictorial model was created for precise Couinaud landmark localization.
  • The system was evaluated on 225 non-contrast T1-weighted MRIs against benchmark techniques and ground truth.

Main Results:

  • The proposed personalized model significantly outperformed the benchmark method in landmark placement and segment volume estimation.
  • Superior performance was observed in 5/8 landmarks and 7/8 Couinaud segments.
  • The framework demonstrated enhanced accuracy in anatomical delineation.

Conclusions:

  • The developed system is explainable, modality-agnostic, and robust to new data without retraining.
  • It offers enhanced scalability for diverse clinical contexts.
  • The framework has the potential to significantly improve Couinaud accuracy, streamline workflows, and optimize liver surgery planning and monitoring.