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Diffusion MRI tractography parcellation classifies streamlines intoanatomical fiber tracts to enable quantification and visualization for clinicaland scientific applications. Current tractography parcellation methods relyheavily on registration, but registration inaccuracies can affect parcellationand the computational cost of registration is high for large-scale datasets.Recently, deep-learning-based methods have been proposed for tractographyparcellation using various types of representations for streamlines. However,these methods only focus on the information from a single streamline, ignoringgeometric relationships between the streamlines in the brain. We proposeTractCloud, a registration-free framework that performs whole-braintractography parcellation directly in individual subject space. We propose anovel, learnable, local-global streamline representation that leveragesinformation from neighboring and whole-brain streamlines to describe the localanatomy and global pose of the brain. We train our framework on a large-scalelabeled tractography dataset, which we augment by applying synthetic transformsincluding rotation, scaling, and translations. We test our framework on fiveindependently acquired datasets across populations and health conditions.TractCloud significantly outperforms several state-of-the-art methods on alltesting datasets. TractCloud achieves efficient and consistent whole-brainwhite matter parcellation across the lifespan (from neonates to elderlysubjects, including brain tumor patients) without the need for registration.The robustness and high inference speed of TractCloud make it suitable forlarge-scale tractography data analysis. Our project page is available athttps://tractcloud.github.io/.

TractCloud: Registration-free tractography parcellation with a novel local-global streamline point cloud representation