White Matter Fiber Segmentation Using Functional Varifolds
This work addresses the need for improved fiber bundle segmentation in neuroscience, particularly for studying neurodegenerative diseases, but it appears incremental as it builds on existing clustering methods with a new metric.
The paper tackled the problem of grouping white matter fibers from dMRI data by proposing a novel computational model called functional varifolds, which incorporates both geometry and microstructure measures (e.g., GFA) into a metric for clustering fibers, and presented a preliminary analysis using HCP data.
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.