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

429
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...
429
Imaging Studies for Cardiovascular System III: X-Ray01:20

Imaging Studies for Cardiovascular System III: X-Ray

553
The most common cardiovascular diagnostic test is an X-ray. It produces images of the heart, blood vessels, and adjacent structures.
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...
553

You might also read

Related Articles

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

Sort by
Same author

Evaluation of Indigenous Latex Agglutination Assay based on Recombinant Pasteurella Lipoprotein E (rPlpE) As Antigen for Detection of Anti <i>Mannheimia Haemolytica</i> - IgG Antibodies.

Archives of Razi Institute·2025
Same author

Surgical outcomes of total duct excision in the diagnosis and management of nipple discharge.

Annals of the Royal College of Surgeons of England·2024
Same author

Reporting of paediatric osteoporotic vertebral fractures in Duchenne muscular dystrophy and potential impact on clinical management: the need for standardised and structured reporting.

Pediatric radiology·2023
Same author

Unpaid caregiving for people following hip fracture: longitudinal analysis from the English Longitudinal Study of Ageing.

European geriatric medicine·2023
Same author

Pediatric Cardiology Fellowship Standards for Training in Exercise Medicine and Curriculum Outline.

Pediatric cardiology·2022
Same author

What is Known About Critical Congenital Heart Disease Diagnosis and Management Experiences from the Perspectives of Family and Healthcare Providers? A Systematic Integrative Literature Review.

Pediatric cardiology·2022

Related Experiment Video

Updated: Mar 6, 2026

Two-Dimensional X-Ray Angiography to Examine Fine Vascular Structure Using a Silicone Rubber Injection Compound
05:26

Two-Dimensional X-Ray Angiography to Examine Fine Vascular Structure Using a Silicone Rubber Injection Compound

Published on: January 7, 2019

6.3K

Vessel extraction in X-ray angiograms using deep learning.

E Nasr-Esfahani, S Samavi, N Karimi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 9, 2017
    PubMed
    Summary

    This study introduces a deep learning method using convolutional neural networks (CNN) to improve coronary artery disease (CAD) diagnosis by enhancing vessel detection in X-ray angiograms, leading to superior vessel segmentation performance.

    More Related Videos

    Sample Preparation for Computed Tomography-based Three-dimensional Visualization of Murine Hind-limb Vessels
    04:35

    Sample Preparation for Computed Tomography-based Three-dimensional Visualization of Murine Hind-limb Vessels

    Published on: October 7, 2021

    2.7K
    Visualization and Quantitative Analysis of Embryonic Angiogenesis in Xenopus tropicalis
    06:05

    Visualization and Quantitative Analysis of Embryonic Angiogenesis in Xenopus tropicalis

    Published on: May 25, 2017

    8.9K

    Related Experiment Videos

    Last Updated: Mar 6, 2026

    Two-Dimensional X-Ray Angiography to Examine Fine Vascular Structure Using a Silicone Rubber Injection Compound
    05:26

    Two-Dimensional X-Ray Angiography to Examine Fine Vascular Structure Using a Silicone Rubber Injection Compound

    Published on: January 7, 2019

    6.3K
    Sample Preparation for Computed Tomography-based Three-dimensional Visualization of Murine Hind-limb Vessels
    04:35

    Sample Preparation for Computed Tomography-based Three-dimensional Visualization of Murine Hind-limb Vessels

    Published on: October 7, 2021

    2.7K
    Visualization and Quantitative Analysis of Embryonic Angiogenesis in Xenopus tropicalis
    06:05

    Visualization and Quantitative Analysis of Embryonic Angiogenesis in Xenopus tropicalis

    Published on: May 25, 2017

    8.9K

    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Cardiology

    Background:

    • Coronary artery disease (CAD) is a leading global cause of death.
    • X-ray angiography is the standard for CAD diagnosis but yields low-quality, noisy images.
    • Accurate vessel enhancement and segmentation are crucial for CAD diagnosis.

    Purpose of the Study:

    • To propose a deep learning approach for precise vessel region detection in angiograms.
    • To enhance the diagnostic accuracy of coronary artery disease (CAD).

    Main Methods:

    • Utilized a deep convolutional neural network (CNN) for image analysis.
    • Preprocessed angiograms to improve contrast.
    • Trained the CNN on 1,040,000 pixel patches to differentiate vessel and background regions.

    Main Results:

    • The proposed deep learning method demonstrated superior performance in extracting vessel regions.
    • Achieved enhanced vessel segmentation in low-quality angiographic images.

    Conclusions:

    • Deep learning, specifically CNNs, offers a powerful tool for improving vessel detection in coronary angiography.
    • This approach holds significant potential for enhancing the diagnosis of coronary artery disease (CAD).