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 conditions.
“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]
The Tractometer definition of a VC is extremely restrictive for current tractography algorithms, as it requires streamlines not to exit the area of the ground truth bundle at any point and to terminate exactly within the endpoint region that is defined by the dilated ground truth fiber endpoints.
“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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Self-citationReal-time multi-peak tractography for instantaneous connectivity display
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Learn to Track: Deep Learning for Tractography
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Self-citationHigh Angular Resolution Diffusion Imaging (HARDI)
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Self-citationParallel Transport Tractography
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Clinical applications of magnetic resonance imaging based functional and structural connectivity
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Metrics reloaded: Recommendations for image analysis validation
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Self-citationFiltering in tractography using autoencoders (FINTA)
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Self-citationActive delineation of Meyer’s loop using oriented priors through MAGNEtic Tractography (MAGNET)
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Self-citationA Machine Learning Based Approach to Fiber Tractography Using Classifier Voting
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Global tractography with embedded anatomical priors for quantitative connectivity analysis
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Track Orientation Density Imaging (TODI) and Track Orientation Distribution (TOD) based tractography
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Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model
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DeepTract: A Probabilistic Deep Learning Framework for White Matter Fiber Tractography
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Mitigating gyral bias in cortical tractography via asymmetric fiber orientation distributions
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Track-to-Learn: A general framework for tractography with deep reinforcement learning
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Self-citationThe role of diffusion tractography in refining glial tumor resection
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Self-citationQi, X., & Arfanakis, K. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117462 · Cited by 39 · see citing works
The role of the arcuate and middle longitudinal fasciculi in speech perception in noise in adulthood
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Self-citationJi, C., Li, J., Han, Q., Lin, J., Yang, T., Chen, Z., & Zhang, J. (2016) · Brain and Behavior · doi:10.1002/brb3.512 · Cited by 38 · see citing works
A new compression format for fiber tracking datasets
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Self-citationFiber pathways supporting early literacy development in 5-8-year-old children.
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Automated tract extraction via atlas based Adaptive Clustering
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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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Towards deep learning for connectome mapping: A block decomposition framework
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CACTUS: a computational framework for generating realistic white matter microstructure substrates
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Physical and digital phantoms for validating tractography and assessing artifacts
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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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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
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- 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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