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

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...

You might also read

Related Articles

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

Sort by
Same author

Real-world treatment patterns and determinants of therapy in systemic sclerosis: findings from the German Network for SSc cohort.

Arthritis research & therapy·2026
Same author

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
Same author

CoRe: Joint Optimization with Contrastive Learning for Medical Image Registration.

Sensors (Basel, Switzerland)·2026
Same author

Influence of a passive shoulder exoskeleton on drilling performance in women- a cross-sectional study.

Scientific reports·2026
Same author

Altered bilateral knee flexor synergies after total and unicompartmental knee arthroplasty resemble non-surgical osteoarthritis synergies.

Clinical biomechanics (Bristol, Avon)·2026
Same author

Instantaneous and Cumulative Knee Joint Loading in Cycling With and Without Medial Knee Osteoarthritis.

Scandinavian journal of medicine & science in sports·2026

Related Experiment Video

Updated: May 7, 2026

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

7.9K

ConvexAdam: Self-Configuring Dual-Optimization-Based 3D Multitask Medical Image Registration.

Hanna Siebert, Christoph Grosbrohmer, Lasse Hansen

    IEEE Transactions on Medical Imaging
    |September 16, 2024
    PubMed
    Summary

    This study introduces a fast, versatile medical image registration method using feature extraction and dual optimization. It achieves high accuracy across diverse tasks with minimal training, enhancing anatomical alignment.

    More Related Videos

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.1K
    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
    02:09

    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

    Published on: April 12, 2024

    549

    Related Experiment Videos

    Last Updated: May 7, 2026

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
    05:05

    Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

    Published on: November 23, 2019

    7.9K
    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities
    07:13

    Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

    Published on: October 27, 2023

    1.1K
    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
    02:09

    Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

    Published on: April 12, 2024

    549

    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Computational Anatomy

    Background:

    • Accurate medical image registration is crucial for aligning anatomical structures, enabling precise analysis and comparison.
    • Current deep learning methods often require extensive training data and lack versatility across different anatomical regions and imaging modalities.
    • Existing approaches struggle to balance speed, accuracy, and adaptability for multitask medical image registration.

    Purpose of the Study:

    • To develop a versatile and efficient medical image registration method adaptable to various tasks and datasets.
    • To reduce the reliance on extensive training data and complex learning procedures common in deep learning registration.
    • To create a self-configuring framework for medical image registration with automatic hyperparameter selection.

    Main Methods:

    • Utilizes semantic or hand-crafted image features combined with a coupled convex and Adam-based instance optimization for deformation field computation.
    • Employs pre-trained semantic feature extraction models tailored to specific datasets.
    • Introduces a rapid, automatic hyperparameter selection procedure for self-configuration based on validation data.

    Main Results:

    • The proposed method demonstrates effective alignment of medical image data across diverse tasks with minimal learning.
    • Achieved competitive results, ranking highly on the Learn2Reg challenge leaderboards.
    • The dual optimization approach combined with automatic hyperparameter tuning provides a fast and accurate registration solution.

    Conclusions:

    • The presented multitask medical image registration framework offers a versatile and efficient alternative to traditional deep learning methods.
    • The approach successfully addresses the limitations of current methods regarding training data requirements and adaptability.
    • This work provides a robust and self-configuring solution for precise anatomical alignment in medical imaging.