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Cited by 664Advanced Neuroimaging Techniques and ApplicationsMedical Image Segmentation TechniquesFunctional Brain Connectivity Studies

TractSeg - Fast and accurate white matter tract segmentation

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Wasserthal, Jakob, Neher, Peter, Maier-Hein, Klaus H.·2018·NeuroImage
DOI 10.1016/j.neuroimage.2018.07.070
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Abstract

The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines is a unique combination of tools which enables the in-vivo delineation and analysis of anatomically well-known tracts. This, however, currently requires complex, computationally intensive processing pipelines which take a lot of time to set up. TractSeg is a novel convolutional neural network-based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without using tractography, image registration or parcellation. We demonstrate that the proposed approach is much faster than existing methods while providing unprecedented accuracy, using a population of 105 subjects from the Human Connectome Project. We also show initial evidence that TractSeg is able to generalize to differently acquired data sets for most of the bundles. The code and data are openly available at https://github.com/MIC-DKFZ/TractSeg/ and https://doi.org/10.5281/zenodo.1088277, respectively.

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  • Manual streamline selection is considered the gold standard for delineating anatomical fiber tracts in tractography, for validating other tract identification techniques, and for benchmarked comparisons and algorithm training.

    “Manual streamline selection is considered to be the gold standard to delineate anatomical fiber tracts in tractography and has been widely used to validate other anatomical tract identification techniques (Poulin et al., 2019; Pujol et al., 2015; Xie et al., 2020).”

    From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Wasserthal et al., 2018

    The Introduction describes manual virtual dissection as the most commonly employed way to segment white matter tracts, and §3.3 compares manual dissections with TractSeg and other methods. But §4.2 says the paper’s reference tracts are not real ground truth. The paper supports manual delineation and its use in comparisons, not the unqualified description of manual selection as a gold standard. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The tremendous amounts of high-impact publications in top journals dealing with deep learning in medical imaging illustrate the value of the methods.

    “This is illustrated by the tremendous amounts of high-impact publications in top-journals dealing with deep learning in medical imaging (for example [17, 21, 30, 32, 40, 90, 162, 137, 140, 273, 285], all published in 2018).”

    From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [273]

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Cited by

Sorted by citations

Authors
Wasserthal, J., Neher, P., & Maier-Hein, K. H.
Affiliations
German Cancer Research Center (DE)
Corresponding author
Maier-Hein, Klaus H.
ORCID
Venue
NeuroImage
Publisher
Elsevier BV
Published
2018-12
Volume
183
Pages
239-253
ISSN
1053-8119 (ISSN-L)
DOI
10.1016/j.neuroimage.2018.07.070
OpenAlex
W2803890652
Cited by
664
References
5
Citations by year
2026: 97 · 2025: 141 · 2024: 96 · 2023: 99 · 2022: 87 · 2021: 74 · 2020: 40 · 2019: 20 · 2018: 10
FWCI
24.87
Type
journal article
Language
en
Open access
Licence
Locations
  • NeuroImage (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
  • Deutsche Forschungsgemeinschaft (MA 6340/12-1, MA 6340/10-1)
  • National Institute of Mental Health (R01MH084898-01A1, 1U01 MH097435)
  • National Institutes of Health
  • NIH Blueprint for Neuroscience Research
  • McDonnell Center for Systems Neuroscience
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Keywords
white matter tract segmentation, fiber orientation distribution function, convolutional neural networks, diffusion-weighted imaging, white matter fiber bundles, Human Connectome Project, cross-dataset generalization
MeSH terms
Deep Learning, Adult, Humans, Nerve Net, Diffusion Tensor Imaging, Neuroimaging, Connectome, White Matter

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