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 III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

84
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...
84
Computed Tomography01:10

Computed Tomography

7.0K
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...
7.0K
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

517
Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
517

You might also read

Related Articles

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

Sort by
Same author

SMARCAL1 is a candidate therapeutic target for ALT-positive tumors.

Genes & development·2026
Same author

Deep Learning-Based Multiclass Classification of Mitral Valve Etiologies Using Limited B-Mode and Color Doppler Echocardiography: Internal and External Validation.

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography·2026
Same author

Epidemiology and clinical characteristics of invasive group A streptococcal infection in the Republic of Korea, 2015-2024: a nationwide multicenter study.

The Lancet regional health. Western Pacific·2026
Same author

Corrigendum: Paenibacillus marinisediminis sp. nov., a bacterium isolated from marine sediment.

Journal of microbiology (Seoul, Korea)·2026
Same author

A novel mouse model of cerebral microbleeds by targeted Col4a1 editing in adult brain microvessels.

Brain : a journal of neurology·2026
Same author

Immunogenicity and Safety of a Full-Dose Regimen of Cell Culture-Derived Quadrivalent Inactivated Influenza Vaccine in Children Aged 6-35 Months: Results from a Multinational Phase 3 Randomised Controlled Trial.

Vaccines·2026

Related Experiment Video

Updated: Oct 11, 2025

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K

Deep Learning-Based Image Conversion Improves the Reproducibility of Computed Tomography Radiomics Features: A

Seul Bi Lee1, Yeon Jin Cho1, Youngtaek Hong2

  • 1From the Department of Radiology, Seoul National University Hospital.

Investigative Radiology
|November 28, 2021
PubMed
Summary

Deep learning image conversion enhances computed tomography (CT) radiomics feature reproducibility across diverse imaging protocols and scanners. This AI-driven approach significantly improves feature consistency, aiding reliable medical image analysis.

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

1.4K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Related Experiment Videos

Last Updated: Oct 11, 2025

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics
10:17

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Published on: January 8, 2018

13.3K
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

1.4K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.4K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiomics

Background:

  • Radiomics analysis relies on reproducible quantitative features extracted from medical images.
  • Variability in computed tomography (CT) imaging protocols, reconstruction kernels, and scanners can significantly impact radiomics feature reproducibility.
  • Improving the consistency of radiomics features is crucial for reliable clinical applications and multi-center studies.

Purpose of the Study:

  • To evaluate the effectiveness of a deep learning-based image conversion technique for enhancing the reproducibility of CT radiomics features.
  • To assess the performance of the developed algorithm across various CT protocols, reconstruction kernels, and scanner types.

Main Methods:

  • An abdominal phantom with liver nodules was used for the study.
  • A residual feature aggregation network was developed for CT image conversion.
  • Radiomics features (first-order, second-order, wavelet) were extracted from original and synthetic images across 8 different CT protocols.
  • Concordance correlation coefficient (CCC) was used to assess measurement variability compared to ground-truth images.

Main Results:

  • Deep learning image conversion improved radiomics feature reproducibility by 83.3% in ROI-based analysis across 8 protocols.
  • 62.0% of radiomics features showed increased CCC after image synthesis, with a significant increase in 26.9%.
  • First-order radiomics features demonstrated the highest improvement in reproducibility (79.9%) compared to second-order and wavelet features.

Conclusions:

  • Deep learning-based image conversion effectively enhances radiomics feature reproducibility.
  • The developed model shows promise for standardizing radiomics analysis across different CT acquisition settings.
  • This technique can potentially improve the reliability and generalizability of radiomics in clinical practice.