Fiber tractography using machine learning
Open accessPublished version of a preprint
Abstract
We present a fiber tractography approach based on a random forest classification and voting process, guiding each step of the streamline progression by directly processing raw diffusion-weighted signal intensities. For comparison to the state-of-the-art, i.e. tractography pipelines that rely on mathematical modeling, we performed a quantitative and qualitative evaluation with multiple phantom and in vivo experiments, including a comparison to the 96 submissions of the ISMRM tractography challenge 2015. The results demonstrate the vast potential of machine learning for fiber tractography.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recent advances in machine-learning-driven tractography show great potential in improving the specificity of tractograms.
“Recent advances in machine-learning-driven tractography also show great potential in improving the specificity of tractograms59, 60.”
From Tractography-based connectomes are dominated by false-positive connections (2016) · cites this paper as [60]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 70 · Cited by 68
Research works printed but not linked: 2
- Basser, P.J., 1998. Fiber-tractography via diffusion tensor MRI, in: Proc. International Society for Magnetic Resonance in Medicine.
- Götz, Michael, Weber, Christian, Stieltjes, Bram, Maier-Hein, K.H., 2014. Learning from Small Amounts of Labeled Data in a Brain Tumor Classification Task, in: Second Workshop on Transfer and Multi-Task Learning: Theory Meets Practice, Neural Information Processing Systems (NIPS). Montreal, Canada.
- Authors
- Neher, P. F., Côté, M.-A., Houde, J.-C., Descoteaux, M., & Maier-Hein, K. H.
- Affiliations
- German Cancer Research Center (DE)
- Université de Sherbrooke (CA)
- Corresponding author
- Neher, Peter F.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2017-09
- Volume
- 158
- Pages
- 417-429
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2951290748
- Cited by
- 68
- References
- 70
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 6 · 2023: 2 · 2022: 7 · 2021: 10 · 2020: 12 · 2019: 4 · 2018: 7 · 2017: 2
- FWCI
- 5.26
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- NeuroImage (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- 16 NIH Institutes and Centers
- McDonnell Center
- Collaborative Research Center (SFB/TRR 125)
- German Research Foundation (INST 35/1120-1)
- DFG (MA 6340/10-1, MA 6340/12-1)
- 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
- fiber tractography, diffusion MRI, random forest, machine learning, white matter fiber tracking
- MeSH terms
- Machine Learning, Brain Mapping, Humans, Diffusion Tensor Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
