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Chiari malformation type 1 is associated with a smaller fourth ventricle volume - a multi-cohort replication study.

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The 1 match
  1. [1] § Methods ↔ dl+direct.sh, lines 80–155 · score 0.53 · FreeSurfer, DiReCT, space, anatomy, voxels, DL

Paper

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The authors' code

Shell · 157 lines · 4.6 KB · BSD-3-Clause · 1 match

  1. #!/bin/bash
  3. usage() {
  4. cat << EOF
  5. Usage: dl+direct [-h] [-s subject] [-b [-i inv2_file]] [-n] [-m model_file] [-k] T1_FILE OUTPUT_DIR
  6. Process T1_FILE (nifti) with dl+direct and put results into OUTPUT_DIR.
  7. Input is expected to be a skull-stripped T1w MRI. You may specify --bet to remove
  8. the skull (using hd-bet).
  10. optional arguments:
  11. -h|--help show this usage
  12. -s|--subject subject-id (written to .csv results)
  13. -b|--bet Skull-stripping using hd-bet
  14. -i|--mp2rage-inv2 Use given 2nd inversion recovery image from an MP2Rage to generate brain mask
  15. -n|--no-cth Skip cortical thickness (DiReCT), just perform segmentation
  16. -m|--model Use given trained model
  17. -k|--keep Keep intermediate files
  18. -l|--lowmem Use less memory (use fp16 for ensembling)
  20. EOF
  21. exit 0
  22. }
  24. invalid() {
  25. echo "ERROR: Invalid argument $1"
  26. usage 1
  27. }
  29. die() {
  30. RET=!?
  31. echo "ERROR (${RET}): $1"
  32. if [ ${RET} -eq 137 ] ; then
  33. echo "Likely out-of-memory. Try with '--lowmem' option"
  34. fi
  35. exit 1
  36. }
  38. # defaults
  39. SUBJECT_ID="subj_id"
  40. DO_SKULLSTRIP=0
  41. DO_CT=1
  42. KEEP_INTERMEDIATE=0
  43. LOW_MEM_ARG=""
  44. MODEL_ARGS=""
  45. MP2RAGE_INV2=""
  46. if [ -z "${ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS}" ] ; then
  47. # number of threads for DiReCT
  48. export ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS=4
  49. fi
  51. # Parse arguments
  52. POSITIONAL=()
  53. while [[ $# -gt 0 ]]; do
  54. case "${1}" in
  55. -h|--help) usage 0;;
  56. -s|--subject) shift; SUBJECT_ID="$1" ;;
  57. -b|--bet) DO_SKULLSTRIP=1 ;;
  58. -i|--mp2rage-inv2) shift; MP2RAGE_INV2=$1 ;;
  59. -n|--no-cth) DO_CT=0 ;;
  60. -m|--model) shift; MODEL_ARGS="--model $1" ;;
  61. -k|--keep) KEEP_INTERMEDIATE=1 ;;
  62. -l|--lowmem) LOW_MEM_ARG="--lowmem True" ;;
  63. -*) invalid "$1" ;;
  64. *) POSITIONAL+=("$1") ;;
  65. esac
  66. shift
  67. done
  69. # Restore positional parameters
  70. set -- "${POSITIONAL[@]}"
  72. if [ $# -lt 2 ] ; then
  73. usage 1
  74. fi
  76. T1=$1
  77. DST=$2
  78. SCRIPT_DIR=`dirname $0`/src
  80. # check prerequisites
  81. [[ -f ${T1} ]] || die "Invalid input volume: ${T1} not found"
  82. [[ ${DO_SKULLSTRIP} -eq 0 ]] || [[ "`which hd-bet`X" != "X" ]] || die "hd-bet not found. Install it from https://github.com/MIC-DKFZ/HD-BET"
  84. mkdir -p ${DST} || die "Could not create target directory ${DST}"
  86. echo
  87. echo "If you are using DL+DiReCT in your research, please cite:"
  88. cat ${SCRIPT_DIR}/../doc/cite.md
  89. echo
  91. # convert into freesurfer space (resample to 1mm voxel, orient to LIA)
  92. python ${SCRIPT_DIR}/conform.py "${T1}" "${DST}/T1w_norm.nii.gz"
  94. HAS_GPU=`python -c 'import torch; print(torch.cuda.is_available() or (hasattr(torch.backends, "mps") and torch.backends.mps.is_available()))'`
  95. if [ ${HAS_GPU} != 'True' ] ; then
  96. echo "WARNING: No GPU/CUDA device found. Running on CPU might take some time..."
  97. fi
  100. # Skull-stripping
  101. if [ ${DO_SKULLSTRIP} -gt 0 ] ; then
  102. # skull-strip using HD-BET
  103. BET_OPTS=""
  104. if [ "${MP2RAGE_INV2}x" != "x" ] ; then
  105. echo "Using ${MP2RAGE_INV2} to create brain mask"
  106. BET_INPUT_VOLUME=${DST}/T1w_mp2rage_INV2_norm.nii.gz
  107. BET_OPTS=" --mp2rage-inv2 ${BET_INPUT_VOLUME}"
  108. python ${SCRIPT_DIR}/conform.py ${MP2RAGE_INV2} "${BET_INPUT_VOLUME}"
  109. fi
  110. IN_VOLUME=${DST}/T1w_norm_noskull.nii.gz
  111. BET_INPUT_VOLUME=${DST}/T1w_norm.nii.gz
  112. MASK_VOLUME=${DST}/T1w_norm_noskull_mask.nii.gz
  113. export PYTORCH_ENABLE_MPS_FALLBACK=1
  115. python ${SCRIPT_DIR}/bet.py ${BET_OPTS} "${BET_INPUT_VOLUME}" "${IN_VOLUME}" || die "hd-bet failed"
  116. else
  117. # Assume input is already skull-stripped
  118. IN_VOLUME=${DST}/T1w_norm.nii.gz
  119. MASK_VOLUME=${IN_VOLUME}
  120. fi
  122. # cropping
  123. IN_VOLUME_CROP=${DST}/T1w_norm_noskull_cropped.nii.gz
  124. python ${SCRIPT_DIR}/crop.py "${MASK_VOLUME}" "${IN_VOLUME}" "${IN_VOLUME_CROP}"
  127. # DeepScan segmentation
  128. python ${SCRIPT_DIR}/DeepSCAN_Anatomy_Newnet_apply.py ${LOW_MEM_ARG} ${MODEL_ARGS} "${IN_VOLUME_CROP}" "${DST}" "${SUBJECT_ID}" || die "Segmentation failed"
  130. if [ ${DO_CT} -gt 0 ] ; then
  131. # DiReCT
  132. python ${SCRIPT_DIR}/DiReCT.py "${DST}" "${DST}" || die "DiReCT failed"
  134. # extract stats
  135. THICK_VOLUME=${DST}/T1w_thickmap.nii.gz
  136. python ${SCRIPT_DIR}/extract_stats.py "${THICK_VOLUME}" "${DST}/seg.nii.gz" "${DST}/softmax_seg.nii.gz" "${SUBJECT_ID}"
  137. fi
  139. FS_ARGS=""
  140. # uncrop to original size
  141. if [ ${DO_CT} -gt 0 ] ; then
  142. python ${SCRIPT_DIR}/crop.py --revert 1 "${MASK_VOLUME}" "${THICK_VOLUME}" "${DST}/T1w_norm_thickmap.nii.gz"
  143. FS_ARGS=" ${DST}/T1w_norm_thickmap.nii.gz:colormap=heat"
  144. fi
  145. python ${SCRIPT_DIR}/crop.py --revert 1 "${MASK_VOLUME}" "${DST}/softmax_seg.nii.gz" "${DST}/T1w_norm_seg.nii.gz"
  147. # cleanup
  148. if [ ${KEEP_INTERMEDIATE} -eq 0 ] ; then
  149. rm -f ${DST}/{boundary*.nii.gz,?mprob*.nii.gz,seg.nii.gz,seg_*.nii.gz,softmax_seg.nii.gz,T1w_norm_noskull_*.nii.gz,T1w_thickmap.nii.gz}
  150. fi
  152. echo
  153. echo "Done, you may view the results with:"
  154. echo -e "\tfreeview ${DST}/T1w_norm.nii.gz ${DST}/T1w_norm_seg.nii.gz:colormap=lut ${FS_ARGS}"
  155. echo

dl+direct.sh at commit c750348, under BSD-3-Clause · at the source

Overview

Authors: Jarod L Roland1,2, Thanda Meehan1, Alexandra Gabliani1, Scott Marek2,3, Nico Dosenbach4, Joshua S Shimony3, Nilay Jakati1, Yi Li5, Leo Sugrue5, Jennifer M Strahle1, Chenyang Lu2,6, David D Limbrick7, Gabe Haller1,2
ORCID iDs: Jarod L Roland, Thanda Meehan
  1. Taylor Family Department of Neurosurgery, Washington University School of Medicine,St. Louis, MO USA
  2. AI for Health Institute, Washington University in St Louis,St. Louis, MO USA
  3. Department of Radiology, Washington University School of Medicine,St. Louis, MO USA
  4. Department of Neurology, Washington University School of Medicine,St. Louis, MO USA
  5. Department of Radiology, University of California San Francisco,San Francisco, CA USA
  6. Department of Computer Science and Engineering, Washington University School of Medicine,St. Louis, MO USA
  7. Department of Neurological Surgery, Virginia Commonwealth University,Richmond, VA USA
Institutions: Washington University in St. Louis (United States); University of California, San Francisco (United States); Virginia Commonwealth University (United States)
Journal: Scientific reports, volume 16, issue 1, article 28407
Dates: received 4 August 2025; accepted 2 June 2026; published online 21 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41598-026-56778-x · PMID 42324379 · PMCID PMC13562637 · OpenAlex W7165492161
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism)
Methods: Statistics
Keywords: Chiari I malformation, Ventricle, Magnetic resonance imaging, Segmentation, Anatomy, Medical research, Neurology, Neuroscience
MeSH: Arnold-Chiari Malformation*, Fourth Ventricle*, Adolescent, Adult, Female, Humans, Magnetic Resonance Imaging, Male, Reproducibility of Results, Retrospective Studies, Young Adult (* major topic)
Topic: Spinal Dysraphism and Malformations (Public Health, Environmental and Occupational Health, Medicine), according to OpenAlex
Funding: NIH (NS131131); National Institutes of Health,United States (NS133486)
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

Chiari Malformation Type 1 (CM1) is canonically defined by ectopic position of the cerebellar tonsils with additional anatomic variations described inconsistently. Effect on fourth ventricle volume is controversial with prior studies reporting disparate results and methodologically limited to single institution series that hinder generalizability. This limitation was addressed utilizing multiple data sources, longitudinal replication, and reproducible methods using both FreeSurfer and the deep learning-based DL+DiReCT tools for automated volumetrics. First, we analyzed a local retrospective clinical cohort of individuals with CM1 and controls. Using data from the Adolescent Brain Cognitive Development (ABCD) Study, we analyzed volumes at baseline then replicated at two longitudinal timepoints. We then utilized an independent deep learning tool to demonstrate reproducibility with ABCD baseline data. Next, we again replicated with a prospective cohort from the Redefining Chiari (RC) study and compared to controls from the Human Connectome Project Young Adult (HCP-YA) study. Finally, we analyzed a heterogenous dataset from the Park-Reeves Syringomyelia Research Consortium (PRSRC) with comparison to ABCD baseline controls. Our aim was to test the hypothesis of a relationship between fourth ventricle size and CM1. Across all datasets, timepoints, and segmentation tools, we consistently found CM1 associated with smaller fourth ventricle volume. Our findings robustly demonstrate that a smaller fourth ventricle volume is an anatomical feature associated with CM1 at the group level. Fourth ventricle volume in CM1 may provide additional insights into pathophysiology but will require further study to fully elucidate its clinical importance.

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

Repositories

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

jarodroland/FourthVentricleVolumeCM1

License: none: the authors keep all their rights
State: the link is dead, verified on 27 September 2026
Evidence: found in the paper
Software Heritage: not archived
Found in: the text, “Methods”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link is dead
  • 27 September 2026: the link is dead
At the source: github.com/jarodroland/FourthVentricleVolumeCM1

SCAN-NRAD/DL-DiReCT

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c750348c569f9dc1c8321048c1ab374b2daca61d, 15 October 2024
Languages: Python (10), Shell (3)
Size: 27 files, 13 scripts
Software Heritage: archived
Found in: the text, “Methods”
Holds: README, license file, environment (pyproject.toml), documentation
Not found: CITATION.cff, tests, continuous integration
Tools: NumPy (7 files), NiBabel (6 files), pandas (5 files), PyTorch (2 files), SciPy (2 files), ANTs (1 file), PyRadiomics (1 file), scikit-image (1 file), SimpleITK (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
15 files
At the source: github.com/SCAN-NRAD/DL-DiReCT

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;
  • 13 scripts, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • 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

Datasets cited

Data availability

Data from the ABCD Study are availabe via the NIH Brain Development Cohorts (NBDC) Data Sharing Platform (https://nbdc.nida.nih.gov/). Data from the HCP-YA Study are available from the Human Connectome website (https://www.humanconnectome.org/). Other data used in this analysis can be accessed via the asscociated Open Science Framework project page (https://osf.io/cn5ej/?view_only=99fe51ca00b84c178ab59e1a6cc53475).

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 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 8 keywords, 11 MeSH terms, 2 funders, 49 references.

Cite

This paper

Roland, J. L., Meehan, T., Gabliani, A., Marek, S., Dosenbach, N., Shimony, J. S., Jakati, N., Li, Y., Sugrue, L., Strahle, J. M., Lu, C., Limbrick, D. D., & Haller, G. (2026). Chiari malformation type 1 is associated with a smaller fourth ventricle volume - a multi-cohort replication study. Scientific reports, 16(1), 28407. https://doi.org/10.1038/s41598-026-56778-x

BibTeX

@article{roland2026chiari,
author = {Roland, Jarod L and Meehan, Thanda and Gabliani, Alexandra and Marek, Scott and Dosenbach, Nico and Shimony, Joshua S and Jakati, Nilay and Li, Yi and Sugrue, Leo and Strahle, Jennifer M and Lu, Chenyang and Limbrick, David D and Haller, Gabe},
title = {{Chiari malformation type 1 is associated with a smaller fourth ventricle volume - a multi-cohort replication study}},
journal = {Scientific reports},
year = {2026},
month = jun,
volume = {16},
number = {1},
pages = {28407},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-56778-x},
url = {https://doi.org/10.1038/s41598-026-56778-x},
pmid = {42324379},
pmcid = {PMC13562637}
}

RIS

TY - JOUR
AU - Roland, Jarod L
AU - Meehan, Thanda
AU - Gabliani, Alexandra
AU - Marek, Scott
AU - Dosenbach, Nico
AU - Shimony, Joshua S
AU - Jakati, Nilay
AU - Li, Yi
AU - Sugrue, Leo
AU - Strahle, Jennifer M
AU - Lu, Chenyang
AU - Limbrick, David D
AU - Haller, Gabe
TI - Chiari malformation type 1 is associated with a smaller fourth ventricle volume - a multi-cohort replication study
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/06/21
VL - 16
IS - 1
SP - 28407
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-56778-x
UR - https://doi.org/10.1038/s41598-026-56778-x
LA - en
ER -

CSL-JSON

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