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

Ultrasonography01:17

Ultrasonography

6.4K
Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...
6.4K
Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

69
IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...
69

You might also read

Related Articles

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

Sort by
Same author

Oridonin protects the lung against hyperoxia-induced injury in a mouse model.

Undersea & hyperbaric medicine : journal of the Undersea and Hyperbaric Medical Society, Inc·2017
Same author

Sarcomatoid Renal Cell Carcinoma Has a Distinct Molecular Pathogenesis, Driver Mutation Profile, and Transcriptional Landscape.

Clinical cancer research : an official journal of the American Association for Cancer Research·2017
Same author

Cell type-selective imaging and profiling of newly synthesized proteomes by using puromycin analogues.

Chemical communications (Cambridge, England)·2017
Same author

A cheat sheet to navigate the complex maze of pharmaceutical exclusivities in Europe.

Pharmaceutical patent analyst·2017
Same author

Hydroxamic Acids as Chemoselective (ortho-Amino)arylation Reagents via Sigmatropic Rearrangement.

Angewandte Chemie (International ed. in English)·2017
Same author

Nerve Growth Factor Is Associated With Sexual Pain in Women With Endometriosis.

Reproductive sciences (Thousand Oaks, Calif.)·2017

Related Experiment Video

Updated: Sep 24, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
04:23

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

2.0K

Unsupervised Convolutional Neural Network for Motion Estimation in Ultrasound Elastography.

Xingyue Wei, Yuanyuan Wang, Lin Ge

    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    |May 2, 2022
    PubMed
    Summary
    This summary is machine-generated.

    A novel unsupervised deep learning method, MaskFlownet-based unsupervised convolutional neural network (MF-UCNN), enhances motion estimation in ultrasound elastography (USE). This approach significantly reduces computation time while maintaining high accuracy, eliminating the need for ground truth data.

    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

    3.0K
    A Novel Application of Musculoskeletal Ultrasound Imaging
    10:53

    A Novel Application of Musculoskeletal Ultrasound Imaging

    Published on: September 17, 2013

    24.3K

    Related Experiment Videos

    Last Updated: Sep 24, 2025

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
    04:23

    A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

    Published on: April 21, 2023

    2.0K
    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

    3.0K
    A Novel Application of Musculoskeletal Ultrasound Imaging
    10:53

    A Novel Application of Musculoskeletal Ultrasound Imaging

    Published on: September 17, 2013

    24.3K

    Area of Science:

    • Medical Imaging
    • Biomedical Engineering
    • Artificial Intelligence

    Background:

    • High-quality motion estimation is critical for ultrasound elastography (USE).
    • Traditional methods like normalized cross correlation (NCC) and global ultrasound elastography (GLUE) are computationally intensive.
    • Supervised deep learning networks require extensive ground truth data, which is difficult to obtain for USE.

    Purpose of the Study:

    • To develop a fast and accurate unsupervised deep learning method for motion estimation in USE.
    • To reduce the computational cost associated with traditional motion estimation techniques.
    • To overcome the limitations of supervised learning by eliminating the need for ground truth data.

    Main Methods:

    • Proposed a MaskFlownet-based unsupervised convolutional neural network (MF-UCNN).
    • Input data includes concatenated RF, envelope, and B-mode images (pre- and post-deformation).
    • Outputs are axial and lateral displacement fields, optimized using a loss function incorporating image similarity and displacement smoothness.

    Main Results:

    • MF-UCNN demonstrated superior performance compared to MPWC-Net++, RFMPWC-Net, GLUE, and NCC.
    • Achieved higher signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in simulations, phantom, and in vivo experiments.
    • Significantly reduced computation time while maintaining high-quality motion estimation.

    Conclusions:

    • MF-UCNN offers a promising unsupervised approach for efficient and accurate motion estimation in USE.
    • Its ability to forgo ground truth data makes it highly practical for clinical applications.
    • The method has substantial potential to advance the field of ultrasound elastography.