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 Experiment Video

Updated: Jan 18, 2026

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

3.3K

DG-TTA: Out-of-Domain Medical Image Segmentation Through Augmentation, Descriptor-Driven Domain Generalization, and

Christian Weihsbach1,2, Christian N Kruse3, Alexander Bigalke4

  • 1Institute of Medical Informatics, University of Lübeck, 23562 Lübeck, Germany.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

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

3d freehand ultrasound reconstruction by reference-based point cloud registration.

International journal of computer assisted radiology and surgery·2025
Same author

AcquisitionFocus: Joint Optimization of Acquisition Orientation and Cardiac Volume Reconstruction Using Deep Learning.

Sensors (Basel, Switzerland)·2024
Same author

Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation.

Sensors (Basel, Switzerland)·2023
Same author

Learning-based three-dimensional registration with weak bounding box supervision.

Journal of medical imaging (Bellingham, Wash.)·2022
Same author

Learning a Metric for Multimodal Medical Image Registration without Supervision Based on Cycle Constraints.

Sensors (Basel, Switzerland)·2022
Same author

Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients.

International journal of computer assisted radiology and surgery·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles
This summary is machine-generated.

This study introduces a novel method for medical image segmentation, improving deep learning model performance on unseen data. The approach combines domain-generalized pre-training with test-time adaptation for high-quality results across different imaging types.

Area of Science:

  • Medical Imaging
  • Deep Learning
  • Computer Vision

Background:

  • Pre-trained medical deep learning segmentation models struggle with out-of-domain images.
  • Poor segmentation quality hinders clinical applications of AI in medical imaging.

Purpose of the Study:

  • To develop a robust method for domain-generalized pre-training and test-time adaptation in medical image segmentation.
  • To achieve high-quality segmentation performance on unseen medical imaging domains.

Main Methods:

  • Utilized a robust generalizing descriptor (SSC) and intensity augmentation (GIN) for domain-generalized pre-training.
  • Implemented test-time adaptation using a consistency scheme with augmentation-descriptor combination for unseen scans.
  • Evaluated performance on five public datasets (3D CT and MRI) across abdominal, spine, and cardiac imaging.
Keywords:
domain generalizationdomain-invariant descriptorstest-time adaptation

More Related Videos

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

Related Experiment Videos

Last Updated: Jan 18, 2026

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

3.3K
Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1.0K

Main Results:

  • Significant improvements in segmentation performance across cross-domain scenarios (CT to MRI).
  • Achieved substantial Dice score increases in abdominal (+46.2, +28.2), spine (+72.9), and cardiac (+14.2, +55.7) imaging (p < 0.001).
  • Demonstrated effective bridging of domain gaps with a compact and efficient methodology.

Conclusions:

  • The proposed method enables high-quality medical image segmentation in unseen domains.
  • Domain-generalized pre-training and test-time adaptation significantly enhance model generalization.
  • The approach allows optimal and independent use of source and target data.