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A rapid streamline-based extension of Tractfinder for white matter tract segmentation.

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Paper

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The authors' code

Python · 103 lines · 3.5 KB · no license

  2. # Copyright (c) 2008-2019 the MRtrix3 contributors.
  3. #
  4. # This Source Code Form is subject to the terms of the Mozilla Public
  5. # License, v. 2.0. If a copy of the MPL was not distributed with this
  6. # file, You can obtain one at http://mozilla.org/MPL/2.0/.
  7. #
  8. # Covered Software is provided under this License on an "as is"
  9. # basis, without warranty of any kind, either expressed, implied, or
  10. # statutory, including, without limitation, warranties that the
  11. # Covered Software is free of defects, merchantable, fit for a
  12. # particular purpose or non-infringing.
  13. # See the Mozilla Public License v. 2.0 for more details.
  14. #
  15. # For more details, see http://www.mrtrix.org/.
  17. import os, sys
  19. try:
  20. # since importlib code below only works on Python 3.5+
  21. # https://stackoverflow.com/a/50395128
  22. if sys.version_info < (3,5):
  23. raise ImportError
  25. import importlib.util
  27. def imported(lib_path):
  28. try:
  29. spec = importlib.util.spec_from_file_location('mrtrix3', os.path.join (lib_path, 'mrtrix3', '__init__.py'))
  30. module = importlib.util.module_from_spec (spec)
  31. sys.modules[spec.name] = module
  32. spec.loader.exec_module (module)
  33. return True
  34. except (ImportError, FileNotFoundError):
  35. return False
  37. except ImportError:
  38. try:
  39. import imp
  40. except ImportError:
  41. print ('failed to import either imp or importlib module!')
  42. sys.exit(1)
  44. def imported(lib_path):
  45. success = False
  46. fp = None
  47. try:
  48. fp, pathname, description = imp.find_module('mrtrix3', [ lib_path ])
  49. imp.load_module('mrtrix3', fp, pathname, description)
  50. success = True
  51. except ImportError:
  52. pass
  53. finally:
  54. if fp:
  55. fp.close()
  56. return success
  59. # First: check the MRTRIX_HOME environment variable
  60. if imported (os.path.join (os.environ['MRTRIX_HOME'], 'lib')) \
  61. if 'MRTRIX_HOME' in os.environ else False:
  62. pass
  64. # Can the MRtrix3 Python modules be found based on their relative location to this file?
  65. # Note that this includes the case where this file is a softlink within an external module,
  66. # which provides a direct link to the core installation
  67. elif not imported (os.path.normpath (os.path.join ( \
  68. os.path.dirname (os.path.realpath (__file__)), os.pardir, 'lib') )):
  70. # If this file is a duplicate, which has been stored in an external module,
  71. # we may be able to figure out the location of the core library using the
  72. # build script.
  74. # case 1: build is a symbolic link:
  75. if not imported (os.path.join (os.path.dirname (os.path.realpath ( \
  76. os.path.join (os.path.dirname(__file__), os.pardir, 'build'))), 'lib')):
  78. # case 2: build is a file containing the path to the core build script:
  79. try:
  80. with open (os.path.join (os.path.dirname(__file__), os.pardir, 'build')) as fp:
  81. for line in fp:
  82. build_path = line.split ('#',1)[0].strip()
  83. if build_path:
  84. break
  85. except IOError:
  86. pass
  88. if not imported (os.path.join (os.path.dirname (build_path), 'lib')):
  90. # Last resort: find the MRtrix3 library via mrconvert on the PATH
  91. import shutil
  92. _mrconvert = shutil.which ('mrconvert')
  93. if not (_mrconvert and imported (os.path.normpath (os.path.join (
  94. os.path.dirname (os.path.realpath (_mrconvert)), os.pardir, 'lib')))):
  96. sys.stderr.write('''
  97. ERROR: Unable to locate MRtrix3 Python modules
  99. For detailed instructions, please refer to:
  100. https://mrtrix.readthedocs.io/en/latest/tips_and_tricks/external_modules.html
  101. ''')
  102. sys.stderr.flush()
  103. sys.exit(1)

mrtrix3.py at commit 2475f4b, no license · at the source

Overview

Authors: Dana Kanel1, Fiona Young2, Kiran K Seunarine1, Chris A Clark1, Kristian Aquilina3, Jonathan D Clayden1
ORCID iDs: Kristian Aquilina
  1. Developmental Imaging and Biophysics Section, UCL GOS Institute of Child Health, London, United Kingdom
  2. Software Engineering and Artificial Intelligence Science Technology Platform, The Francis Crick Institute, London, United Kingdom
  3. Department of Neurosurgery, Great Ormond Street Hospital for Children, London, United Kingdom
Institutions: University College London (United Kingdom); The Francis Crick Institute (United Kingdom); Great Ormond Street Hospital (United Kingdom)
Journal: Frontiers in neuroimaging, volume 5, article 1873040
Dates: received 5 May 2026; accepted 22 June 2026; published online 6 July 2026
Type: Methods article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fnimg.2026.1873040 · PMID 42519527 · PMCID PMC13381243 · OpenAlex W7167459982
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), other condition (population), methods / tools (subfield)
Methods: Connectivity, fMRI & imaging, Physiology & signal measures
Keywords: dMRI, paediatric brain tumours, pre-operative, tract segmentation, tractography
Topic: Advanced Neuroimaging Techniques and Applications (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Funding: National Institute for Health and Care Research; CHILDREN with CANCER UK (23-353); University College London
Citations: not cited yet (Europe PMC); 20 references in the paper

Abstract

Accurate delineation of white matter tracts is critical in the pre-operative assessment of paediatric brain tumour patients, where preservation of eloquent pathways directly influences surgical planning and functional outcomes. Tractfinder is a recently introduced automated method for white matter tract segmentation in tumour patients, but its voxel-based (mask) outputs limit compatibility with streamline-based tractography tools, visualisation workflows, and downstream analytical frameworks. Here we introduce Tractfinder-constrained Tractography (TcT), a streamline-based extension that constrains probabilistic tractography to the probability maps produced by Tractfinder, generating streamline representations while preserving the speed and automation that make Tractfinder clinically appealing. We evaluated TcT in ten pre-operative paediatric patients with supratentorial tumours, targeting three clinically relevant tracts – the corticospinal tract, arcuate fasciculus, and optic radiation. Spatial agreement between TcT and conventional tractography was assessed using Bundle Adjacency (BA). Mean BA scores across all three tracts ranged from 2.1 to 2.6 mm, comparing favourably against published inter-protocol benchmarks for conventional probabilistic tractography (4.3 mm), and approaching within-protocol variability. The TcT pipeline was fully automated, required no manual region-of-interest placement, and completed in approximately 5–15 min per subject compared to 1–2 h for conventional tractography. These results demonstrate that TcT produces streamline-based tract segmentations with good spatial agreement to conventional tractography, while offering substantially reduced processing time and operator burden.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above.

tractfinder/tractfinder

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2475f4bd82013cb3e265c5aee9a294636273cd7d, 5 June 2026
Languages: Python (6)
Size: 13 files, 6 scripts
Software Heritage: not archived
Found in: “Data availability statement”
Holds: README, environment (pyproject.toml, uv.lock)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (3 files), MRtrix3 (2 files), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/tractfinder/tractfinder

fionaEyoung/tractfinder

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 2475f4bd82013cb3e265c5aee9a294636273cd7d, 5 June 2026
Languages: Python (6)
Size: 13 files, 6 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, environment (pyproject.toml, uv.lock)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (3 files), MRtrix3 (2 files), scikit-image (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
7 files
At the source: github.com/fionaEyoung/tractfinder

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 12 scripts, each with its path and the digest of its content;
  • no match between paragraphs and code yet;
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

Scripts for running Tractfinder and generating custom tract atlases are available at: https://github.com/tractfinder/tractfinder. Tract orientation atlases (and corresponding training streamlines) for the AF, CST, and OR are openly available for non-commercial use: https://doi.org/10.5281/zenodo.10149873. The clinical neuroimaging data from Great Ormond Street Hospital cannot be publicly shared to maintain patient confidentiality. Requests to access these datasets should be directed to .

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Funding: added National Institute for Health and Care Research; CHILDREN with CANCER UK: 23-353; Great Ormond Street Institute of Child Health

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 6 authors, 5 keywords, 18 references.

Cite

This paper

Kanel, D., Young, F., Seunarine, K. K., Clark, C. A., Aquilina, K., & Clayden, J. D. (2026). A rapid streamline-based extension of Tractfinder for white matter tract segmentation. Frontiers in neuroimaging, 5, 1873040. https://doi.org/10.3389/fnimg.2026.1873040

BibTeX

@article{kanel2026rapid,
author = {Kanel, Dana and Young, Fiona and Seunarine, Kiran K and Clark, Chris A and Aquilina, Kristian and Clayden, Jonathan D},
title = {{A rapid streamline-based extension of Tractfinder for white matter tract segmentation}},
journal = {Frontiers in neuroimaging},
year = {2026},
month = jul,
volume = {5},
pages = {1873040},
publisher = {Frontiers Media SA},
issn = {2813-1193},
doi = {10.3389/fnimg.2026.1873040},
url = {https://doi.org/10.3389/fnimg.2026.1873040},
pmid = {42519527},
pmcid = {PMC13381243}
}

RIS

TY - JOUR
AU - Kanel, Dana
AU - Young, Fiona
AU - Seunarine, Kiran K
AU - Clark, Chris A
AU - Aquilina, Kristian
AU - Clayden, Jonathan D
TI - A rapid streamline-based extension of Tractfinder for white matter tract segmentation
T2 - Frontiers in neuroimaging
J2 - Front Neuroimaging
PY - 2026
DA - 2026/07/06
VL - 5
SP - 1873040
SN - 2813-1193
PB - Frontiers Media SA
DO - 10.3389/fnimg.2026.1873040
UR - https://doi.org/10.3389/fnimg.2026.1873040
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fnimg.2026.1873040",
"type": "article-journal",
"title": "A rapid streamline-based extension of Tractfinder for white matter tract segmentation",
"container-title": "Frontiers in neuroimaging",
"author": [
{
"family": "Kanel",
"given": "Dana"
},
{
"family": "Young",
"given": "Fiona"
},
{
"family": "Seunarine",
"given": "Kiran K"
},
{
"family": "Clark",
"given": "Chris A"
},
{
"family": "Aquilina",
"given": "Kristian"
},
{
"family": "Clayden",
"given": "Jonathan D"
}
],
"container-title-short": "Front Neuroimaging",
"volume": "5",
"page": "1873040",
"DOI": "10.3389/fnimg.2026.1873040",
"PMID": "42519527",
"PMCID": "PMC13381243",
"ISSN": "2813-1193",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fnimg.2026.1873040",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
6
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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