GeoLab: Geometry-based Tractography Parcellation of Superficial White Matter
This work addresses the need for better tractography parcellation methods in superficial white matter, which is important for clinical research, though it appears incremental as it builds on existing approaches like RecoBundles.
The authors tackled the problem of parcellating superficial white matter (SWM) bundles, which are less studied than long-range connections, by proposing GeoLab, a geometry-based method that efficiently segments hundreds of short white matter bundles from a subject, extracting more bundles with a higher number of streamlines compared to other state-of-the-art methods.
Superficial white matter (SWM) has been less studied than long-range connections despite being of interest to clinical research, andfew tractography parcellation methods have been adapted to SWM. Here, we propose an efficient geometry-based parcellation method (GeoLab) that allows high-performance segmentation of hundreds of short white matter bundles from a subject. This method has been designed for the SWM atlas of EBRAINS European infrastructure, which is composed of 657 bundles. The atlas projection relies on the precomputed statistics of six bundle-specific geometrical properties of atlas streamlines. In the spirit of RecoBundles, a global and local streamline-based registration (SBR) is used to align the subject to the atlas space. Then, the streamlines are labeled taking into account the six geometrical parameters describing the similarity to the streamlines in the model bundle. Compared to other state-of-the-art methods, GeoLab allows the extraction of more bundles with a higher number of streamlines.