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

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

You might also read

Related Articles

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

Sort by
Same author

UniTransAD: Unified Translation Framework for Anomaly Detection in Brain MRI.

IEEE transactions on medical imaging·2026
Same author

Progression of interstitial lung abnormalities and its impact on mortality in patients with lung cancer resection.

Chinese medical journal pulmonary and critical care medicine·2026
Same author

Discriminating nodular pulmonary alveolar proteinosis from lung adenocarcinoma with radiomic model and multimodal model.

Therapeutic advances in respiratory disease·2026
Same author

U<sup>2</sup>AD: Uncertainty-based unsupervised anomaly detection framework for detecting T2 hyperintensity in MRI spinal cord.

Medical image analysis·2026
Same author

Continuous sPatial-temporal deformable image registration and 4D frame interpolation.

Medical physics·2025
Same author

A Dual-Stream Mamba With Contrastive Representation for Multimodal Deformable Registration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Related Experiment Video

Updated: Jul 15, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Contrastive Discrepancy: A label-free metric for deformable image registration supporting testing-time hyperparameter

Xia Li1, Jihe Li2, Weijie Wang3

  • 1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China; Department of Computer Science, ETH Zurich, Zurich, 8092, Switzerland.

Medical Image Analysis
|July 13, 2026
PubMed
Summary

We developed Contrastive Discrepancy (CD), a new label-free metric for evaluating deformable image registration (DIR). CD enables automatic hyperparameter tuning without manual annotations, improving DIR model performance for personalized medicine.

Keywords:
Deformable image registrationEvaluation metricHyperparameter tuningLabel-free evaluationModel selection

More Related Videos

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
07:50

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

Related Experiment Videos

Last Updated: Jul 15, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
07:50

Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation

Published on: January 27, 2023

Area of Science:

  • Medical Image Analysis
  • Computational Anatomy
  • Machine Learning for Medical Imaging

Background:

  • Deformable image registration (DIR) is vital for medical image analysis.
  • Evaluating DIR is challenging due to the lack of ground-truth deformations.
  • Current metrics are either label-dependent (costly) or label-free (less accurate).

Purpose of the Study:

  • Introduce Contrastive Discrepancy (CD), a novel label-free metric for DIR evaluation.
  • Enable robust DIR model assessment and automatic hyperparameter tuning without manual annotations.
  • Bridge the gap between DIR research and clinical application.

Main Methods:

  • CD measures discrepancy between deformation vector fields (DVFs) from images with varied observations under transformation groups.
  • Grounded in bias-variance trade-off theory and group equivariance.
  • Evaluated across three DIR model families and two public datasets.

Main Results:

  • CD performance mirrors the gold-standard Target Registration Error (TRE).
  • CD significantly outperforms existing label-free DIR metrics.
  • CD enables fully automatic, testing-time hyperparameter selection for DIR models.

Conclusions:

  • CD provides a reliable, label-free method for DIR evaluation and hyperparameter tuning.
  • Automated tuning using CD allows dynamic optimization for individual patient data.
  • CD facilitates the precise, personalized application of DIR in clinical workflows.