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arXiv 2303.01147eess.IVcs.CV

GeoLab: Geometry-based Tractography Parcellation of Superficial White Matter

Université Paris-Saclay, CEA, Neurospin(巴黎-萨克雷大学,法国原子能委员会,神经自旋);Harvard Medical School(哈佛医学院);University of Sydney(悉尼大学);University College London (UCL)(伦敦大学学院)

Nabil Vindas, Nicole Labra Avila, Fan Zhang, Tengfei Xue, Lauren J. O'Donnell, Jean-François Mangin

英文摘要:

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.

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