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 Videos

Riemannian elasticity: a statistical regularization framework for non-linear registration.

X Pennec1, R Stefanescu, V Arsigny

  • 1INRIA Sophia - Projet Epidaure, 2004 Route des Lucioles BP 93, 06902 Sophia Antipolis, France. Xavier.Pennec@sophia.inria.fr

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|May 12, 2006
PubMed
Summary
This summary is machine-generated.

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

The Ruminant Farm Systems (RuFaS) model is a platform to support future research and actions for sustainable dairy farming.

JDS communications·2026
Same author

Artificial intelligence in presymptomatic neurological diseases: Bridging normal variation and prodromal signatures.

Revue neurologique·2025
Same author

Mortality and histopathology in sheepshead minnow (Cyprinodon variegatus) larvae exposed to pectenotoxin-2 and Dinophysis acuminata.

Aquatic toxicology (Amsterdam, Netherlands)·2023
Same author

Knowledge, actual and potential use of HIV self-sampling testing kits among MSM recruited in eight European countries.

HIV medicine·2018
Same author

MONITORING SLOWLY EVOLVING TUMORS.

Proceedings. IEEE International Symposium on Biomedical Imaging·2017
Same author

From Digital Anatomy to Virtual Scalpels and Image Guided Therapy.

Yearbook of medical informatics·2016
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
See all related articles

This study introduces statistical Riemannian elasticity, a novel regularization method for inter-subject registration. It quantifies deformation variability using strain tensor statistics for more accurate non-rigid image alignment.

Area of Science:

  • Medical image analysis
  • Computational anatomy
  • Differential geometry

Background:

  • Inter-subject registration requires robust regularization to model transformation variability.
  • Existing statistical models for deformation variability are difficult to integrate into registration algorithms.
  • Elastic energy, often used for regularization, measures deviation from rigid transformation.

Purpose of the Study:

  • To develop a statistically consistent framework for quantifying deformation variability in inter-subject registration.
  • To introduce a novel regularization criterion, statistical Riemannian elasticity, for non-rigid registration.
  • To enable more accurate and robust image registration by accounting for anisotropic deformations.

Main Methods:

  • Interpreting elastic energy as the distance of the Green-St Venant strain tensor to the identity.

Related Experiment Videos

  • Employing a Riemannian metric instead of a Euclidean metric for deformation quantification.
  • Computing the mean and covariance matrix of the strain tensor from a population of transformations.
  • Utilizing Mahalanobis distance with computed statistics as a regularization criterion.

    • Main Results:

    • A consistent statistical framework for quantifying deformation in non-linear transformations was established.
    • The proposed statistical Riemannian elasticity criterion effectively handles anisotropic deformations.
    • The new criterion is inverse-consistent and demonstrates ease of implementation in non-rigid registration algorithms.
    • Preliminary results indicate improved performance in registration tasks.

    Conclusions:

    • Statistical Riemannian elasticity offers a principled and effective approach to regularization in inter-subject registration.
    • This method provides a robust way to model and utilize transformation variability.
    • The framework facilitates the development of more accurate and adaptable non-rigid registration algorithms.