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Neuroimage. 2018 Apr 15;170:283-295. doi: 10.1016/j.neuroimage.2017.07.015. Epub 2017 Jul 13.

Recognition of white matter bundles using local and global streamline-based registration and clustering.

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Department of Intelligent Systems Engineering, School of Informatics and Computing, Indiana University, Bloomington, USA. Electronic address:
Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Canada.
Brain Development Imaging Lab (BDIL), Department of Psychology, San Diego State University, USA; Groupe d' Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, CEA Université de Bordeaux, Bordeaux, France.
Groupe d' Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives, UMR5293, CNRS, CEA Université de Bordeaux, Bordeaux, France.
Division of Neurosurgery and Neuro-Oncology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Québec, Canada.
Research Center on Aging and Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Québec, Canada.


Virtual dissection of diffusion MRI tractograms is cumbersome and needs extensive knowledge of white matter anatomy. This virtual dissection often requires several inclusion and exclusion regions-of-interest that make it a process that is very hard to reproduce across experts. Having automated tools that can extract white matter bundles for tract-based studies of large numbers of people is of great interest for neuroscience and neurosurgical planning. The purpose of our proposed method, named RecoBundles, is to segment white matter bundles and make virtual dissection easier to perform. This can help explore large tractograms from multiple persons directly in their native space. RecoBundles leverages latest state-of-the-art streamline-based registration and clustering to recognize and extract bundles using prior bundle models. RecoBundles uses bundle models as shape priors for detecting similar streamlines and bundles in tractograms. RecoBundles is 100% streamline-based, is efficient to work with millions of streamlines and, most importantly, is robust and adaptive to incomplete data and bundles with missing components. It is also robust to pathological brains with tumors and deformations. We evaluated our results using multiple bundles and showed that RecoBundles is in good agreement with the neuroanatomical experts and generally produced more dense bundles. Across all the different experiments reported in this paper, RecoBundles was able to identify the core parts of the bundles, independently from tractography type (deterministic or probabilistic) or size. Thus, RecoBundles can be a valuable method for exploring tractograms and facilitating tractometry studies.


Bundles; Clustering; Diffusion MRI; Extraction; Fascicles; Fiber tracking; Recognition; Streamlines; Tracts; Virtual dissection

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