White Matter Fiber Segmentation Using Functional Varifolds

TL;DR

This paper introduces a novel fiber representation called functional varifolds that combines geometry and microstructure measures for improved white matter fiber clustering in dMRI data.

Contribution

It proposes a new computational model for fibers that integrates microstructure and geometry, enabling more comprehensive fiber analysis.

Findings

Functional varifolds effectively incorporate microstructure and geometry.

Preliminary analysis on HCP data demonstrates the method's potential.

Clustering results show improved fiber grouping accuracy.

Abstract

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.