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

Comprehensive Performance Testing and External Validation of an AI Algorithm to Detect and Segment Brain Metastases.

Neuro-oncology·2026
Same author

Mind the gap: challenges and future directions for content-based image retrieval in clinical radiology.

Frontiers in radiology·2026
Same author

Disentangled generative uncertainty-aware multi-modal diffusion segmentation of medical images.

Medical image analysis·2026
Same author

[Training and Experience of Healthcare Professionals in Clinical Teaching: A Study in Primary Care in Chile].

Revista medica de Chile·2026
Same author

The Ischemic Stroke Lesion Segmentation Challenge (ISLES)'24 Dataset: A Multimodal Stroke Imaging Dataset with Hyperacute CT, Acute Postinterventional MRI, and 3-month Clinical Outcomes.

Radiology. Artificial intelligence·2026
Same author

Assessing the robustness and clinical evaluation of a deep-learning segmentation model for head and neck cancer.

Frontiers in oncology·2026

Related Experiment Video

Updated: May 28, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Geometry-aware multiscale image registration via OBBTree-based polyaffine log-demons.

Christof Seiler1, Xavier Pennec, Mauricio Reyes

  • 1Institute for Surgical Technology and Biomechanics, University of Bern, Switzerland.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|October 15, 2011
PubMed
Summary

This study introduces a new image registration method that improves accuracy and robustness for anatomical variability analysis. The geometry-aware algorithm adapts to data, offering better results than standard methods, especially for orthopedic images.

More Related Videos

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

Multimodal Approach to Assess Bone Regeneration and Scaffold Performance
06:54

Multimodal Approach to Assess Bone Regeneration and Scaffold Performance

Published on: February 13, 2026

Related Experiment Videos

Last Updated: May 28, 2026

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

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

Multimodal Approach to Assess Bone Regeneration and Scaffold Performance
06:54

Multimodal Approach to Assess Bone Regeneration and Scaffold Performance

Published on: February 13, 2026

Area of Science:

  • Medical image analysis
  • Computational anatomy
  • Biomedical engineering

Background:

  • Non-linear image registration is crucial for analyzing anatomical variability, particularly in morphometric studies.
  • Current methods, like those used for brain imaging, may not be optimal for orthopedic applications due to differing deformation priors.
  • Uninformed priors in registration can lead to local minima, resulting in anatomically implausible solutions.

Purpose of the Study:

  • To develop a robust and anatomically meaningful non-linear image registration algorithm.
  • To improve the accuracy and adaptability of image registration for diverse anatomical structures.
  • To bridge the gap between poly-affine and non-rigid registration techniques.

Main Methods:

  • Proposed a locally affine and geometry-aware registration algorithm.
  • Integrated an OBBTree-based regularization with a multiscale structure into the log-domain demons algorithm.
  • The regularization model utilizes a hierarchy of locally affine transformations.

Main Results:

  • The proposed method demonstrated improved accuracy and robustness in experiments on mandible images.
  • The algorithm showed comparable performance to existing methods with a significantly lower degree of freedom.
  • Using the new method as initialization for the demons algorithm enhanced its performance.

Conclusions:

  • The developed registration algorithm offers a more robust and accurate approach for analyzing anatomical variability.
  • The geometry-aware, locally affine regularization adapts effectively to different data types, including orthopedic images.
  • This work facilitates more sophisticated statistical analysis of registration outcomes and advances non-rigid registration capabilities.