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Cited by 279Advanced Neuroimaging Techniques and ApplicationsFunctional Brain Connectivity StudiesMedical Image Segmentation Techniques

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

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Garyfallidis, Eleftherios, Côté, Marc-Alexandre, Rheault, Francois, Sidhu, Jasmeen, Hau, Janice, Petit, Laurent, Fortin, David, Cunanne, Stephen +1 more·2018·NeuroImage
DOI 10.1016/j.neuroimage.2017.07.015
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Abstract

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.

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  • Fiber clustering methods group tractography streamlines according to their geometric trajectories, describing structural connectivity according to white matter anatomy.

    “They group tractography streamlines according to their geometric trajectories, describing the structural connectivity according to the white matter anatomy (Avila et al., 2019; Chekir et al., 2014; Garyfallidis et al., 2018; Guevara et al., 2012; Li et al., 2010; O’Donnell and Westin, 2007; Román et al., 2017; Ros et al., 2013; Siless et al., 2018; 2020; Tunç et al., 2014; Vázquez et al., 2020; Wu”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Garyfallidis et al., 2018

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Authors
Garyfallidis, E., Côté, M.-A., Rheault, F., Sidhu, J., Hau, J., Petit, L., Fortin, D., Cunanne, S., & Descoteaux, M.
Affiliations
  • Indiana University Bloomington (US)
  • Université de Sherbrooke (CA)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Garyfallidis, Eleftherios
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2018-04
Volume
170
Pages
283-295
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2017.07.015
OpenAlex
W2735700022
Cited by
279
References
59
Citations by year
2026: 22 · 2025: 46 · 2024: 32 · 2023: 39 · 2022: 35 · 2021: 36 · 2020: 27 · 2019: 29 · 2018: 11 · 2017: 2
FWCI
2.85
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, other-oa)
Funders
State Corporation Commission
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Keywords
virtual dissection, tractography, probabilistic tractography, deterministic tractography, brain tumors
MeSH terms
Brain Neoplasms, Computer Simulation, Humans, Image Processing, Computer-Assisted, Pattern Recognition, Automated, Diffusion Tensor Imaging, Neuroimaging, White Matter, Datasets as Topic

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