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

You might also read

Related Articles

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

Sort by
Same author

Anti-OmpC antibodies in Crohn's disease and ulcerative colitis: evidence from a systematic review and meta-analysis.

Crohn's & colitis 360·2026
Same author

Synergistic Effects of Maize Volatiles on Pheromone Trap Captures of the Fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae).

Journal of chemical ecology·2026
Same author

Anaesthetic gas emissions in a tertiary hospital in Pakistan: Behavioural drivers versus technological solutions.

The journal of climate change and health·2026
Same author

Suspected venous oxygen embolism following hydrogen peroxide wound irrigation causing intraoperative asystole.

BMJ case reports·2026
Same author

The effectiveness of mindfulness-based nursing management intervention programme on compassion fatigue, resilience, and social support among oncology nurses: a randomised controlled trial with mediating role of physiological biomarkers.

BMC nursing·2026
Same author

From jealousy to loyalty: the power of brand attachment.

BMC psychology·2026

Related Experiment Video

Updated: May 17, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

333

COUPLED SWIN TRANSFORMERS AND MULTI-APERTURES NETWORK(CSTA-NET) IMPROVES MEDICAL IMAGE SEGMENTATION.

Siyavash Shabani1, Muhammad Sohaib1, Sahar A Mohamed1

  • 1Department of Electrical and Biomedical Engineering, University of Nevada, Reno.

Proceedings. IEEE International Symposium on Biomedical Imaging
|May 14, 2025
PubMed
Summary

This study introduces CSTA-Net, a novel Vision Transformer model for 3D medical image segmentation. It achieves high accuracy, effectively delineating fine details in medical scans.

Keywords:
Distance TransformMedical Image SegmentationVision Transformer

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

2.6K
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

1.7K

Related Experiment Videos

Last Updated: May 17, 2025

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

333
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

2.6K
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

1.7K

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Artificial Intelligence

Background:

  • Vision Transformers (ViTs) show superior performance over traditional methods in visual tasks.
  • 3D medical image segmentation remains a challenging area requiring advanced deep learning models.

Purpose of the Study:

  • To introduce the Coupled Swin Transformers and Multi-Apertures Networks (CSTA-Net) for enhanced 3D medical image segmentation.
  • To improve the delineation of fine details in medical images.

Main Methods:

  • The CSTA-Net architecture integrates Swin Transformer outputs with an Aperture Network.
  • Each aperture network combines global and local feature maps using convolution and fusion blocks.

Main Results:

  • The model was evaluated on the Synapse multi-organ and ACDC datasets.
  • Achieved an average Dice score of 90.19±0.05 on Synapse and 93.77±0.04 on ACDC.
  • Demonstrated effective delineation of fine details.

Conclusions:

  • CSTA-Net advances 3D medical image segmentation using Vision Transformers.
  • The proposed architecture offers a promising approach for accurate and detailed medical image analysis.