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 I: CT and MRI01:14

Imaging Studies I: CT and MRI

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...

You might also read

Related Articles

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

Sort by
Same author

Doppler Radar Sensor-Based Fall Detection Using a Convolutional Bidirectional Long Short-Term Memory Model.

Sensors (Basel, Switzerland)·2024
Same journal

Age-Related Concentric Remodeling and Sex-Dependent Dimensional Variation in Left Ventricular Geometry: A Cardiac Magnetic Resonance Study.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Opportunistic Screening for Low Bone Density Using Automated Vertebral Trabecular CT Attenuation from Low-Dose CT Acquired During FDG PET/CT: A Single-Center Retrospective Study.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Machine Learning-Based Classification of BI-RADS 4 and BI-RADS 5 Microcalcifications in Mammography Combined with DCE-MRI for Malignant-Benign Discrimination.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Image Quality Assessment of Diffusion-Weighted Imaging (DWI) and Its Impact on Apparent Diffusion Coefficient (ADC) as a Quantitative Imaging Biomarker for Predicting Response to Neoadjuvant Chemotherapy in High-Risk Early Breast Cancer.

Tomography (Ann Arbor, Mich.)·2026
Same journal

Relationship Between Cervical Central Canal and Neural Foraminal Dimensions in a Normative Population.

Tomography (Ann Arbor, Mich.)·2026
Same journal

AI-Based Scientific Manuscript Peer Review: Is It Ready for Adoption?

Tomography (Ann Arbor, Mich.)·2026
See all related articles

Related Experiment Video

Updated: Jun 26, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

TASC-SwinMT: Task-Adaptive Synergistic Cross-Task Swin Multi-Task Framework for CT and MRI Image Interpolation and

Yujia Sun1, Yingying Yang1, Nan Bao1

  • 1School of Biomedical Engineering, Northeastern University, Shenyang 110169, China.

Tomography (Ann Arbor, Mich.)
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces TASC-SwinMT, a novel framework for joint medical image interpolation and segmentation. It enhances clinical diagnosis by improving accuracy and reducing computational load through shared feature learning.

Keywords:
Computed TomographyMagnetic Resonance ImagingSwin Transformercross-task interactionfeature alignment fusionimage interpolationmedical image segmentationmulti-task learningtask-aware adapter

More Related Videos

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface
06:15

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface

Published on: September 15, 2023

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

Related Experiment Videos

Last Updated: Jun 26, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface
06:15

Rapid Setup of Tissue Microarray and Tiled Area Imaging on the Multiplexed Ion Beam Imaging Microscope Using the Tile/SED/Array Interface

Published on: September 15, 2023

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

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Computational Anatomy

Background:

  • Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) interpolation and segmentation are vital for clinical applications.
  • Current methods often process these tasks separately, leading to inefficiencies and missed opportunities for feature sharing.
  • This necessitates integrated approaches for improved medical image analysis.

Purpose of the Study:

  • To develop a unified multi-task learning framework for simultaneous medical image interpolation and segmentation.
  • To leverage shared spatial features between interpolation and segmentation tasks.
  • To enhance the accuracy and efficiency of medical image analysis pipelines.

Main Methods:

  • Proposed TASC-SwinMT, a unified multi-task framework using a shared SwinUNet encoder and task-specific decoders.
  • Implemented three modules for cross-task synergistic learning and a dynamic multi-task loss function.
  • Validated on Medical Segmentation Decathlon datasets (Task02_Heart, Task06_Lung).

Main Results:

  • TASC-SwinMT outperformed baseline models in both interpolation and segmentation tasks.
  • Achieved superior performance in lesion boundary depiction, small object segmentation, and inter-slice consistency.
  • Demonstrated significantly reduced computational overhead compared to separate methods.

Conclusions:

  • The proposed cross-task feature sharing and joint optimization strategy proved effective.
  • TASC-SwinMT exhibits strong stability and generalization for clinical medical image analysis.
  • The framework offers a reliable solution for integrated CT and MRI analysis.