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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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“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]
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“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]
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BackgroundMethodInfluentialSelf-citationTractography-based connectomes are dominated by false-positive connections
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“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
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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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