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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]
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]
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Boukadi, M., Marcotte, K., Bedetti, C., Houde, J., Désautels, A., Deslauriers‐Gauthier, S., Chapleau, M., Boré, A., Descoteaux, M., & Brambati, S. M. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.01055 · Cited by 34
Self-citationAdvances in computational and statistical diffusion MRI
O’Donnell, L. J., Daducci, A., Wassermann, D., & Lenglet, C. (2017) · NMR in Biomedicine · doi:10.1002/nbm.3805 · Cited by 32
Rheault, F., Houde, J., & Descoteaux, M. (2017) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2017.00042 · Cited by 30
Self-citationTracking and validation techniques for topographically organized tractography
Aydogan, D. B., & Shi, Y. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.071 · Cited by 27
Mancini, M., Vos, S. B., Vakharia, V. N., O’Keeffe, A. G., Trimmel, K., Barkhof, F., Dorfer, C., Soman, S., Winston, G. P., Wu, C., et al. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101883 · Cited by 26
A multi-scale probabilistic atlas of the human connectome
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Self-citationAssessing connectivity related injury burden in diffuse traumatic brain injury
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Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography
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Data-driven fiber tractography with neural networks
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Simultaneous Multi-Scale Diffusion Estimation and Tractography Guided by Entropy Spectrum Pathways
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CACTUS: a computational framework for generating realistic white matter microstructure substrates
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Generative Sampling in Bundle Tractography using Autoencoders (GESTA)
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Self-citationTractographical model of the cortico‐basal ganglia and corticothalamic connections
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Reducing false positives in tractography with microstructural and anatomical priorsPreprint
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Self-citationCTtrack: A CNN+Transformer-based framework for fiber orientation estimation & tractography
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Tutorial: a guide to diffusion MRI and structural connectomics
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A flocking based method for brain tractography
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Merged Group Tractography Evaluation with Selective Automated Group Integrated Tractography
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Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves
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Self-citationTractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge
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Self-citationAsymmetric fiber trajectory distribution estimation using streamline differential equation
Feng, Y., & He, J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101686 · Cited by 15
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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