The DOI matches its Crossref record; 66 of the 66 references it cites were found in a registry or an index.
Tractometer: Towards validation of tractography pipelines
Abstract
We have developed the Tractometer: an online evaluation and validation system for tractography processing pipelines. One can now evaluate the results of more than 57,000 fiber tracking outputs using different acquisition settings (b-value, averaging), different local estimation techniques (tensor, q-ball, spherical deconvolution) and different tracking parameters (masking, seeding, maximum curvature, step size). At this stage, the system is solely based on a revised FiberCup analysis, but we hope that the community will get involved and provide us with new phantoms, new algorithms, third party libraries and new geometrical metrics, to name a few. We believe that the new connectivity analysis and tractography characteristics proposed can highlight limits of the algorithms and contribute in solving open questions in fiber tracking: from raw data to connectivity analysis. Overall, we show that (i) averaging improves quality of tractography, (ii) sharp angular ODF profiles helps tractography, (iii) seeding and multi-seeding has a large impact on tractography outputs and must be used with care, and (iv) deterministic tractography produces less invalid tracts which leads to better connectivity results than probabilistic tractography.
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In the citing paper
“Previous studies have reported high invalid-connection ratios under simplified conditions26, 38”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [38]
In the citing paper
“The Tractometer definition of a VC is extremely restrictive for current tractography algorithms, as it requires streamlines (1) not to exit the area of the ground truth bundle at any point and (2) to terminate exactly within the endpoint region that is defined by the dilated ground truth fiber endpoints (Supplementary Figs. 8, 9)38.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [38]
Citations
Cites
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Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
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White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
Jones, D. K., Knösche, T. R., & Turner, R. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.06.081 · Cited by 2,402 · cited 1×
“Hence, as mentioned in (Jones et al., 2013), fiber count should be avoided.”
BackgroundTournier, J., Calamante, F., & Connelly, A. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.02.016 · Cited by 2,191
Tuch, D. S. (2004) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20279 · Cited by 1,880
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MRtrix: Diffusion tractography in crossing fiber regions
Tournier, J., Calamante, F., & Connelly, A. (2012) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22005 · Cited by 1,370
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Regularized, fast, and robust analytical Q-ball imaging
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Self-citation
Research works printed but not linked: 9
- Garyfallidis, E., 2012. Toward an accurate brain tractography. Ph.D. thesis, University of Cambridge.
- Girard, G., Chamberland, M., Houde, J.C., Fortin, D., Descoteaux, M., 2012. Neurosurgical tracking at the Sherbrooke Connectivity Imaging Lab ( SCIL ). In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’12) – DTI Challenge, Workshop, pp. 55–73.
- Girard, G., Descoteaux, M., 2012. Anatomical tissue probability priors for tractography. In: International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’12) – Computational Diffusion MRI, Workshop, pp. 174–185.
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- Poupon, C., Laribiere, L., Tournier, G., Bernard, J., Fournier, D., Fillard, P., Descoteaux, M., Mangin, J.F., 2010. A diffusion hardware phantom looking like a coronal brain slice. In: Proceedings of the International Society for Magnetic Resonance in Medicine.
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Cited by
Sorted by citations
Dipy, a library for the analysis of diffusion MRI data
Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., van der Walt, S., Descoteaux, M., Nimmo-Smith, I., & Contributors, D. (2014) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2014.00008 · Cited by 1,371
“It is often useful in dMRI to filter, group or count streamlines based on their interactions with one or several ROIs (Côté et al., 2013).”
BackgroundMethodInfluentialSelf-citationTractography-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-citationDiffusion MRI fiber tractography of the brain
Jeurissen, B., Descoteaux, M., Mori, S., & Leemans, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3785 · Cited by 574
“In fact, a recent simulation study has shown that fiber tracking will produce non‐existing fiber bundles (that were never part of the set of ground‐truth fiber bundles) even with biological priors in place.(13,109) What is alarming is that these artificial bundles approximate true anatomical structures very well: they…”
BackgroundSelf-citationMetrics reloaded: recommendations for image analysis validation
Maier‐Hein, L., Reinke, A., Godau, P., Tizabi, M. D., Buettner, F., Christodoulou, E., Glocker, B., Isensee, F., Kleesiek, J., Kozubek, M., et al. (2024) · Nature Methods · doi:10.1038/s41592-023-02151-z · Cited by 590
Towards quantitative connectivity analysis: reducing tractography biases
Girard, G., Whittingstall, K., Deriche, R., & Descoteaux, M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.04.074 · Cited by 324
Self-citation
Metadata
- Authors
- Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., & Descoteaux, M.
- Affiliations
- Université de Sherbrooke (CA)
- Corresponding author
- Descoteaux, Maxime
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2013-10
- Volume
- 17
- Issue
- 7
- Pages
- 844-857
- ISSN
- 1361-8415 (ISSN-L)
- OpenAlex
- W2013160622
- Cited by
- 228
- References
- 66
- Citations by year
- 2026: 7 · 2025: 19 · 2024: 17 · 2023: 9 · 2022: 17 · 2021: 25 · 2020: 21 · 2019: 21 · 2018: 23 · 2017: 24 · 2016: 18 · 2015: 14 · 2014: 11 · 2013: 2
- FWCI
- 11.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- 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
- tractography, pipeline validation, connectivity analysis, diffusion MRI, spherical deconvolution, Q-ball imaging, seeding strategies, b-value, phantom evaluation
- MeSH terms
- Algorithms, Artificial Intelligence, Brain, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Nerve Fibers, Myelinated, Online Systems, Pattern Recognition, Automated, Sensitivity and Specificity, Software, Reproducibility of Results, Internet, Imaging, Three-Dimensional, Diffusion Tensor Imaging
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