The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
Robust clustering of massive tractography datasets
Abstract
This paper presents a clustering method that detects the fiber bundles embedded in any MR-diffusion based tractography dataset. Our method can be seen as a compressing operation, capturing the most meaningful information enclosed in the fiber dataset. For the sake of efficiency, part of the analysis is based on clustering the white matter (WM) voxels rather than the fibers. The resulting regions of interest are used to define subset of fibers that are subdivided further into consistent bundles using a clustering of the fiber extremities. The dataset is reduced from more than one million fiber tracts to about two thousand fiber bundles. Validations are provided using simulated data and a physical phantom. We see our approach as a crucial preprocessing step before further analysis of huge fiber datasets. An important application will be the inference of detailed models of the subdivisions of white matter pathways and the mapping of the main U-fiber bundles.
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“Conceptually, the principle of inferring connectivity from local orientation fields can lead to problems as soon as pathways overlap, cross, branch, and have complex geometries7, 35, 36.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [36]
In the citing paper
“Regions of white matter bottlenecks have been discussed in the past35 and have been highlighted as critical with respect to tractographic findings36.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [36]
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Cites
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data
Smith, S. M., Jenkinson, M., Johansen‐Berg, H., Rueckert, D., Nichols, T. E., Mackay, C. E., Watkins, K. E., Ciccarelli, O., Cader, M. Z., Matthews, P. M., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.024 · Cited by 6,598
Watersheds in digital spaces: an efficient algorithm based on immersion simulations
Vincent, L. M., & Soille, P. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.87344 · Cited by 5,663
Hierarchical Clustering Schemes
Johnson, S. C. (1967) · Psychometrika · doi:10.1007/bf02289588 · Cited by 4,948
Diffusion tensor imaging: Concepts and applications
Le Bihan, D., Mangin, J., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1076 · Cited by 3,547
Self-citationTournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191
Perisylvian language networks of the human brain
Catani, M., Jones, D. K., & ffytche, D. H. (2005) · Annals of Neurology · doi:10.1002/ana.20319 · Cited by 1,832
Reproducibility of quantitative tractography methods applied to cerebral white matter
Wakana, S., Caprihan, A., Panzenboeck, M. M., Fallon, J. H., Perry, M., Gollub, R. L., Hua, K., Zhang, J., Jiang, H., Dubey, P., et al. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.049 · Cited by 1,686
A diffusion tensor imaging tractography atlas for virtual in vivo dissections.
CATANI, M., & THIEBAUTDESCHOTTEN, M. (2008) · Cortex · doi:10.1016/j.cortex.2008.05.004 · Cited by 1,642
Basser, P. J. (1995) · NMR in Biomedicine · doi:10.1002/nbm.1940080707 · Cited by 1,526
Hua, K., Zhang, J., Wakana, S., Jiang, H., Li, X., Reich, D. S., Calabresi, P. A., Pekar, J. J., van Zijl, P. C. M., & Mori, S. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.053 · Cited by 1,507
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Tractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,376
Self-citationDubois, J., Dehaene-Lambertz, G., Kulikova, S., Poupon, C., Hüppi, P. S., & Hertz-Pannier, L. (2014) · Neuroscience · doi:10.1016/j.neuroscience.2013.12.044 · Cited by 851
Self-citationShort frontal lobe connections of the human brain
Catani, M., Dell’Acqua, F., Vergani, F., Malik, F., Hodge, H., Roy, P. K., Valabrègue, R., & de Schotten, M. T. (2011) · Cortex · doi:10.1016/j.cortex.2011.12.001 · Cited by 828
QuickBundles, a Method for Tractography Simplification
Garyfallidis, E., Brett, M., Correia, M., Williams, G. B., & Nimmo‐Smith, I. (2012) · Frontiers in Neuroscience · doi:10.3389/fnins.2012.00175 · Cited by 315
Worbe, Y., Marrakchi‐Kacem, L., Lecomte, S., Valabrègue, R., Poupon, F., Guevara, P., Tucholka, A., Mangin, J., Vidailhet, M., Lehéricy, S., et al. (2014) · Brain · doi:10.1093/brain/awu311 · Cited by 257
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Metadata
- Authors
- Guevara, P., Poupon, C., Rivière, D., Cointepas, Y., Descoteaux, M., Thirion, B., & Mangin, J.-F.
- Affiliations
- University of Concepción (CL)
- Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
- Université de Sherbrooke (CA)
- Institut national de recherche en sciences et technologies du numérique (FR)
- Corresponding author
- Guevara, P.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2011-02
- Volume
- 54
- Issue
- 3
- Pages
- 1975-1993
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W1966699768
- Cited by
- 157
- References
- 47
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 7 · 2023: 8 · 2022: 9 · 2021: 8 · 2020: 16 · 2019: 8 · 2018: 8 · 2017: 11 · 2016: 15 · 2015: 15 · 2014: 17 · 2013: 13 · 2012: 5
- FWCI
- 6.50
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Région Ile-de-France (France)
- French Embassy in Chile
- CONICYT Chile
- University of Concepción (Chile)
- 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
- diffusion MRI tractography
- MeSH terms
- Adult, Algorithms, Brain, Child, Computer Simulation, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Nerve Fibers, Neural Pathways, Reproducibility of Results, Cluster Analysis, Databases, Factual, Phantoms, Imaging, Data Compression, Diffusion Tensor Imaging
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