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

Shape analysis using a point-based statistical shape model built on correspondence probabilities.

Heike Hufnagel1, Xavier Pennec, Jan Ehrhardt

  • 1Asclepios Project, INRIA, Sophia Antipolis, France. heike.hufnagel@sophia.inria.fr

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 7, 2007
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

Toward real-time alignment of 3D CT and 2D X-ray with multi-stage CNNs.

Computer assisted surgery (Abingdon, England)·2026
Same author

Task-aware multiple instance learning for stress detection from facial video data.

Journal of affective disorders·2026
Same author

Deep learning-based mediastinal lymph node assessment on PET/CT images without pixel-level annotations.

Journal of medical imaging (Bellingham, Wash.)·2026
Same author

Evaluating Imputation Techniques for Survival Data Utilizing Kaplan-Meier Curves.

Studies in health technology and informatics·2025
Same author

Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures.

Machine learning: science and technology·2025
Same author

Neutral sentiment on patient's speech can predict the depressive symptom severity transdiagnostically.

Journal of affective disorders·2025
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 a novel statistical shape model algorithm using evolving correspondence probabilities instead of fixed homologies. This method enhances accuracy in computing mean shape and variation, improving brain structure analysis and classification.

Area of Science:

  • Medical image analysis
  • Computational anatomy
  • Statistical modeling

Background:

  • Computing statistical shape models requires accurate point correspondences between data instances.
  • Assuming fixed homologies can lead to imprecise mean shape and variability results.
  • Existing methods face challenges in robustly determining correspondences for complex shapes.

Purpose of the Study:

  • To develop a novel algorithm for computing generative statistical shape models.
  • To replace exact correspondences with evolving correspondence probabilities for improved accuracy.
  • To establish a unified framework for model parameter computation and optimal adaptation to observations.

Main Methods:

  • Utilizing evolving correspondence probabilities as the basis for a new generative statistical shape model algorithm.

Related Experiment Videos

  • Employing a unified Maximum A Posteriori (MAP) framework for model and nuisance parameter computation.
  • Implementing the Expectation Maximization--Iterative Closest Point (EM-ICP) algorithm for robust and fast registration using probabilistic correspondences.

    • Main Results:

    • Achieved efficient and closed-form solutions for model and nuisance parameters through alternated optimization.
    • Demonstrated the efficiency and well-posedness of the approach through experimental results on brain structure data sets.
    • Successfully extended the algorithm for automatic classification using k-means clustering on synthetic and real brain data.

    Conclusions:

    • The proposed approach offers a more accurate and robust method for computing statistical shape models.
    • The unified MAP framework and EM-ICP algorithm provide efficient parameter estimation and registration.
    • The extended algorithm shows promise for automatic classification tasks in medical image analysis, particularly for brain structures.