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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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Self-citation
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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