  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.
