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

Imaging Studies VII: Vascular Imaging01:19

Imaging Studies VII: Vascular Imaging

294
DefinitionRenal angiography, also known as renal arteriography, is an imaging technique used to obtain a comprehensive view of blood flow and the vascular structure of blood vessels in the kidneys and surrounding areas.PurposeRenal angiography detects blood vessel abnormalities in the kidneys, such as aneurysms, stenosis, thrombosis, vascular tumors, and renal artery stenosis. It evaluates kidney function and guides interventional treatments like angioplasty or stent placement.Pre-Procedure...
294
Imaging Studies for Cardiovascular System IV: CMRI01:21

Imaging Studies for Cardiovascular System IV: CMRI

300
Cardiovascular magnetic resonance imaging, or CMRI, is a non-invasive diagnostic test that employs a magnetic field and radiofrequency waves to create precise images of the heart and arteries. It provides comprehensive information about cardiac anatomy, function, perfusion, and tissue characterization without ionizing radiation.IndicationsCMRI diagnoses various heart conditions, including tissue damage from heart attacks, ischemic heart disease, myocarditis, aortic issues (tears, aneurysms,...
300

You might also read

Related Articles

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

Sort by
Same author

A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.

EJNMMI physics·2026
Same author

Navigator-Based Slice Tracking for Multiband Accelerated Liver DWI.

Magnetic resonance in medicine·2026
Same author

Integration of Conventional and Radiomic Features From Fluorine-18 Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging for Multimodal Prediction of Symptomatic Carotid Atherosclerotic Plaques.

Journal of the American Heart Association·2026
Same author

Temporal-spatial evolution of tumor habitat analysis: a bibliometric study on research hotspots and trends in medical imaging (2014-2025).

Translational cancer research·2026
Same author

Whole-body <sup>18</sup>F-FDG PET/CT identifies subclinical metabolic phenotypes in normoglycemic adults.

European journal of nuclear medicine and molecular imaging·2025
Same author

Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Radiomics Features of Voxel-Wise DCE-MRI Time-Intensity-Curve Profile Maps.

Biomedicines·2025

Related Experiment Video

Updated: Jan 10, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.7K

Clinically oriented deep learning framework for automated vessel wall segmentation in black-blood MRI: a multi-center

Xuetong Tao1,2, Shuai Shen3,4, Long Yang1,2

  • 1Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

European Radiology
|November 22, 2025
PubMed
Summary

This study presents a deep learning framework for accurate vessel wall segmentation in black-blood MR imaging. The method improves cerebrovascular risk assessment for stroke prevention and monitoring.

Keywords:
Carotid arteryCerebrovascular disordersDeep learningMagnetic resonance imagingStroke

More Related Videos

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

3.3K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

Related Experiment Videos

Last Updated: Jan 10, 2026

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies
04:25

Author Spotlight: Bridging Gaps in Anatomy and Establishing a Foundation for Algorithmic Studies

Published on: December 15, 2023

3.7K
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

3.3K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

43.4K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Cardiovascular Research

Background:

  • Accurate segmentation of intracranial and carotid vessel walls is crucial for assessing cerebrovascular disease.
  • Current manual segmentation methods are time-consuming and prone to inter-observer variability.
  • Black-blood magnetic resonance vessel wall imaging (MR-VWI) provides detailed vessel wall information but requires robust segmentation techniques.

Purpose of the Study:

  • To develop and validate a clinically applicable deep learning framework for automated segmentation of intracranial and carotid vessel walls.
  • To enhance the accuracy and reproducibility of vessel wall segmentation in black-blood MR-VWI.
  • To provide a practical tool for streamlining cerebrovascular risk assessment.

Main Methods:

  • A retrospective multi-center study involving 193 patients and high-resolution black-blood MR-VWI data.
  • Development of a deep learning framework incorporating polar coordinate mapping, feature-sharing padding, and a polar Dice loss function.
  • External validation on independent multi-center datasets and the MICCAI 2021 Vessel Wall Segmentation Challenge dataset; interpretability using Grad-CAM.

Main Results:

  • The deep learning model achieved high segmentation accuracy on external test sets, with Dice Similarity Coefficients (DSCs) of 0.928 (outer wall), 0.936 (lumen), and 0.844 (vessel wall).
  • The model significantly outperformed four benchmark networks in boundary and area accuracy.
  • It achieved the highest vessel wall DSC (0.782) on the public MICCAI dataset and demonstrated consistent focus on relevant anatomical boundaries via Grad-CAM.

Conclusions:

  • The developed deep learning-based method enables accurate and reproducible vessel wall segmentation in clinical black-blood MR-VWI.
  • This framework offers a practical solution for cerebrovascular risk assessment, supporting decision-making in stroke prevention and monitoring.
  • Reliable segmentation facilitates objective quantification of intracranial atherosclerosis for early diagnosis and treatment planning.