White Matter Fiber Segmentation Using Functional Varifolds - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

White Matter Fiber Segmentation Using Functional Varifolds

(1, 2) , (3) , (2, 4) , (2) , (2) , (1)


The extraction of fibers from dMRI data typically produces a large number of fibers, it is common to group fibers into bundles. To this end, many specialized distance measures, such as MCP, have been used for fiber similarity. However, these distance based approaches require point-wise correspondence and focus only on the geometry of the fibers. Recent publications have highlighted that using microstructure measures along fibers improves tractography analysis. Also, many neurodegenerative diseases impacting white matter require the study of microstructure measures as well as the white matter geometry. Motivated by these, we propose to use a novel computational model for fibers, called functional varifolds, characterized by a metric that considers both the geometry and microstructure measure (e.g. GFA) along the fiber pathway. We use it to cluster fibers with a dictionary learning and sparse coding-based framework, and present a preliminary analysis using HCP data.
Fichier principal
Vignette du fichier
kumar_varifolds_MICCAI-MFCA_postprint.pdf (1.4 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01589649 , version 1 (18-09-2017)


Attribution - CC BY 4.0


  • HAL Id : hal-01589649 , version 1


Kuldeep Kumar, Pietro Gori, Benjamin Charlier, Stanley Durrleman, Olivier Colliot, et al.. White Matter Fiber Segmentation Using Functional Varifolds. MFCA 2017 - 6th MICCAI workshop on Mathematical Foundations of Computational Anatomy, Sep 2017, Québec, Canada. pp.92-100. ⟨hal-01589649⟩
709 View
195 Download


Gmail Facebook Twitter LinkedIn More