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: Jul 2, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Adaptive deformation decomposition network for unsupervised medical image registration.

Yinghao Li1,2, Jinke Li1,2, Hong Wang1,2

  • 1School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, People's Republic of China.

Biomedical Physics & Engineering Express
|June 30, 2026
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

Resistin, an adipocytokine, offers protection against acute myocardial infarction.

Journal of molecular and cellular cardiology·2007
Same author

Liquid chromatographic analysis of phosphoamino acids at femtomole level using chemical derivatization with N-hydroxysuccinimidyl fluorescein-O-acetate.

Analytica chimica acta·2007
Same author

6-oxy-(acetyl piperazine) fluorescein as a new fluorescent labeling reagent for free fatty acids in serum using high-performance liquid chromatography.

Journal of chromatography. A·2007
Same author

Synthesis and fluorescence properties of 5,7-diphenylquinoline and 2,5,7-triphenylquinoline derived from m-terphenylamine.

Molecules (Basel, Switzerland)·2007
Same author

[Metabolic engineering of terpenoids in plants].

Sheng wu gong cheng xue bao = Chinese journal of biotechnology·2007
Same author

Hedgehog signaling in the murine melanoma microenvironment.

Angiogenesis·2007

This study introduces the Adaptive Deformation Decomposition Network (ADDNet) for medical image registration, enabling accurate alignment of unprepared datasets without prior affine transformation. ADDNet excels in handling complex and large deformations, improving registration accuracy and efficiency.

Area of Science:

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

Background:

  • Deformable registration is crucial in medical image analysis but often relies on pre-processed data, limiting its clinical applicability.
  • Existing methods struggle with direct alignment of unprepared datasets, hindering scalability and integration into clinical workflows.
  • The integration of affine and deformable registration for unaligned data remains a significant challenge.

Purpose of the Study:

  • To investigate the limitations of current registration methods on unprepared medical datasets.
  • To develop a novel registration method capable of handling complex and large deformations directly on unaligned images.
  • To improve the applicability and scalability of deformable registration techniques for clinical use.

Main Methods:

Keywords:
brain MRIcontextual feature fusiondeformation decompositionimage registration

Related Experiment Videos

Last Updated: Jul 2, 2026

Automated Joint Space Detection Improves Bone Segmentation Accuracy
06:45

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

  • Introduction of the Adaptive Deformation Decomposition Network (ADDNet) for unsupervised non-rigid registration.
  • Development of a Deformation Decomposition Module (DDM) to capture multi-scale dependencies and semantic information for large displacement.
  • Design of an Adaptive Contextual Fusion (ACF) module to efficiently fuse global and local features based on deformation complexity.

Main Results:

  • ADDNet demonstrates superior performance in handling both large and small deformations, especially on datasets without affine pre-alignment.
  • Achieved state-of-the-art registration accuracy on LPBA, ABCT, IXI, and Mindboggle datasets, evidenced by best-in-class HD95 and MSE metrics.
  • ADDNet generates more realistic deformation fields, confirming its effectiveness and efficiency in complex registration tasks.

Conclusions:

  • ADDNet offers a robust solution for direct, unsupervised non-rigid registration of unprepared medical image datasets.
  • The network's ability to decompose complex deformations and adaptively fuse features enhances accuracy and efficiency.
  • This method significantly advances the potential for applying advanced registration techniques in real-world clinical settings.