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

DTI segmentation by statistical surface evolution.

Christophe Lenglet1, Mikaël Rousson, Rachid Deriche

  • 1INRIA, Sophia-Antipolis, France. clenglet@sophia.inria.fr

IEEE Transactions on Medical Imaging
|June 14, 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

Diffusion MRI experimental design optimization for microstructure imaging.

Communications biology·2026
Same author

Factors associated with severe neurological sequelae of COVID-19: findings from the multicenter COVID-BRAIN imaging cohort.

Frontiers in human neuroscience·2026
Same author

Modeling of Fiber Orientation-Dependent R1 Relaxation in Human White Matter In Vivo Within The Framework of The Transient Hydrogen Bond Model.

Magnetic resonance in medicine·2026
Same author

A Numerical Alternative to MR Thermometry for Safety Validation of Multi-Channel RF Transmit Coils.

Magnetic resonance in medicine·2026
Same author

Identification of Biological Subtypes of Friedreich Ataxia with Structural MRI-based Machine Learning.

Radiology·2026
Same author

Value of MRI Outcomes for Preventive and Early-Stage Trials in Spinocerebellar Ataxias 1 and 3.

Annals of clinical and translational neurology·2026
Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026
Same journal

Informed-Exploration Reinforcement Learning for Automated Virtual Coronary Intervention Planning.

IEEE transactions on medical imaging·2026
Same journal

4D Reconstruction of Fetal Left Ventricle from Echocardiography via 2.5D Radial Segmentation and Graph-Fourier Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

Generalised Medical Phrase Grounding.

IEEE transactions on medical imaging·2026
Same journal

EndoLRMGS: Combining Large Reconstruction Modelling and Gaussian Splatting for Complete Endoscopic Scene Reconstruction.

IEEE transactions on medical imaging·2026
Same journal

A Neural-Analytical Fusion Scatter Correction Method for Multi-Source CT Using Equivalent High-Order Scatter.

IEEE transactions on medical imaging·2026
See all related articles

Accurate segmentation of cerebral white matter structures from diffusion tensor images (DTI) is improved by using differential geometry. This approach enhances tensor statistics and segmentation quality compared to traditional methods.

Area of Science:

  • Medical Imaging
  • Neuroscience
  • Computational Anatomy

Background:

  • Diffusion Tensor Imaging (DTI) generates tensor-valued images representing water molecule motion.
  • Segmenting white matter structures in DTI is challenging due to the complex nature of tensor data.
  • Existing dissimilarity measures (e.g., Euclidean, Kullback-Leibler) have limitations in accurately characterizing tensor differences.

Purpose of the Study:

  • To investigate the impact of different probability metrics on DTI segmentation quality.
  • To demonstrate that using differential geometrical properties of normal distributions improves segmentation accuracy.
  • To introduce a variational level-set framework for optimal DTI segmentation.

Main Methods:

  • Utilized the differential geometrical properties of the manifold of multivariate normal distributions as a dissimilarity measure.

Related Experiment Videos

  • Introduced a variational formulation within the level-set framework for segmentation.
  • Incorporated statistical assumptions of Gaussian distribution for diffusion tensors within partitions.
  • Accounted for geometric constraints from DTI gradient-detected interfaces.

    • Main Results:

    • The choice of probability metric significantly impacts tensor statistics and segmentation outcomes.
    • Differential geometrical measures yield improved segmentation quality over Euclidean or Kullback-Leibler divergence.
    • The proposed level-set method effectively segments cerebral white matter structures.

    Conclusions:

    • Theoretically grounded dissimilarity measures based on differential geometry enhance DTI segmentation.
    • The variational level-set approach provides a robust framework for analyzing diffusion tensor data.
    • This work advances the accurate segmentation of brain white matter structures from DTI data.