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Portable Ultra-Low Field MRI Deep-Learning Algorithms for White Matter Lesion Segmentation Improve Accuracy and Reflect Clinical Disability in Multiple Sclerosis

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Paper

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The authors' code

Shell · 46 lines · 1.3 KB · Apache-2.0

  1. #!/usr/bin/env bash
  2. # safe-label.sh — label wrapper with a denylist.
  3. #
  4. # Usage: safe-label.sh <number> <add|remove> <label>
  5. #
  6. # Blocks the "ready-for-fix" label so agent workflows cannot apply it and
  7. # thereby trigger the bug-fix PR workflow. All other labels pass through.
  8. # The denylist is intentionally small; this is the structural boundary that
  9. # makes fix-PR creation a human-only decision.
  10. #
  11. # GITHUB_REPOSITORY is provided automatically by GitHub Actions.
  13. set -euo pipefail
  15. if [[ $# -ne 3 ]]; then
  16. echo "Usage: $0 <number> <add|remove> <label>" >&2
  17. exit 2
  18. fi
  20. NUMBER="$1"
  21. ACTION="$2"
  22. LABEL="$3"
  24. # Deny ADD of the ready-for-fix label. REMOVE is allowed so the fix
  25. # workflow can clear the label after opening its PR.
  26. case "$ACTION:$LABEL" in
  27. add:ready-for-fix)
  28. echo "safe-label.sh: refusing to add label 'ready-for-fix' — reserved for maintainers" >&2
  29. exit 1
  30. ;;
  31. esac
  33. REPO="${GITHUB_REPOSITORY:?GITHUB_REPOSITORY must be set}"
  35. case "$ACTION" in
  36. add)
  37. exec gh issue edit "$NUMBER" --repo "$REPO" --add-label "$LABEL"
  38. ;;
  39. remove)
  40. exec gh issue edit "$NUMBER" --repo "$REPO" --remove-label "$LABEL"
  41. ;;
  42. *)
  43. echo "safe-label.sh: unknown action '$ACTION' (expected add|remove)" >&2
  44. exit 2
  45. ;;
  46. esac

safe-label.sh at commit 202f6ba, under Apache-2.0 · at the source

Overview

Authors: Ashley A. Thommana1,2, Corinne A. Donnay1,3, Gina Norato4, María I. Gaitán1, Ludovica Griffanti5, Govind Nair6, Daniel S. Reich1, Serhat V. Okar1
ORCID iDs: Ashley A. Thommana, Corinne A. Donnay, Gina Norato, María I. Gaitán, Ludovica Griffanti, Govind Nair, Daniel S. Reich, Serhat V. Okar
  1. Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
  2. Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, USA
  3. Oxford Centre for Integrative Neuroimaging - University of Oxford, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
  4. Office of Biostatistics, Division of Intramural Research, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
  5. Oxford Centre for Integrative Neuroimaging, Oxford Centre for Human Brain Activity - University of Oxford, Department of Psychiatry, University of Oxford, Oxford, United Kingdom
  6. qMRI Core Facility, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
Institutions: National Institute of Neurological Disorders and Stroke (United States); National Institutes of Health; Thomas Jefferson University (United States); University of Oxford (United Kingdom); National Institutes of Health (United States)
Journal: medRxiv (preprint)
Dates: published online 17 July 2026
Type: Preprint · Language: English
License: CC0
Identifiers: DOI 10.64898/2026.07.15.26357954 · OpenAlex W7169591259
Open access: green, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), multiple sclerosis (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning
Keywords: MRI, multiple sclerosis, portable MRI, low-field MRI, white matter lesions, lesion segmentation, machine learning, deep learning
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: National Institute for Health Research (NIHR) (NIHR203316)
Citations: not cited yet (Europe PMC); 36 references in the paper

Abstract

White matter lesion (WML) identification, assessment, and characterization using magnetic resonance imaging (MRI) are fundamental for diagnosis and monitoring of multiple sclerosis (MS). Portable ultra-low field (pULF) MRI at 64 millitesla (mT) has been shown to visualize WML with at least one dimension greater than 4 mm. An automated WML segmentation tool catered to pULF-MRI can provide standardized and accurate quantitative measurements of WML volume. In this study, we sought to investigate and compare the accuracy of machine-learning (ML) and deep-learning (DL) pULF MRI segmentation tools. Same-day paired pULF (64mT) and high-field (HF, 3T) MRI scans from 84 adults with MS or suspected-MS (mean age±SD: 48±13, 62 females) included T2-FLAIR and T1w images. Reference WML segmentations were manually annotated on pULF T2-FLAIR for all scans, with WML confirmed with registered HF T2-FLAIR. HF reference WML segmentations were created. Four automated segmentation methods were applied to pULF scans: Method for Inter-Modal Segmentation Analysis (MIMoSA), an ML algorithm trained on HF WML masks; WMH-SynthSeg, a convolutional neural network model with flexible segmentation capabilities across field strengths and resolution; nnU-Net, a DL algorithm trained on pULF reference WML masks; and Pseudo-Label Assisted nnU-Net (PLAn), a DL algorithm pre-trained on HF reference WML masks and refined with 64mT reference WML masks. Two models were trained with nnU-Net, one using T2-FLAIR images only (nnU-Net-FL) and one using T1w and T2-FLAIR images (nnU-Net-FL/T1). The same was done with PLAn, creating PLAn-FL and PLAn-FL/T1. The six automated WML segmentation outputs were compared to the manual segmentations to determine Dice Similarity Coefficient (DSC) scores. Associations of WML volume estimates with clinical measures were investigated.

DSC scores with pULF reference WML masks from PLAn-FL (DSC mean±SD: 0.50±0.24) outperformed MIMoSA (0.24±0.20, p<0.0001), WMH-SynthSeg (0.30±0.18, p<0.0001), nnU-Net-FL (0.41±0.24, p<0.0001), and nnU-Net-FL/T1 (0.41 ± 0.26, p = 0.0004). Worse Expanded Disability Status Scale (EDSS) and Scripps Neurologic Rating Scale (SNRS) scores were correlated with higher WML volumes in the pULF and HF reference masks. They were also correlated with WML volumes derived from WHM-SynthSeg, nnU-Net-FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1, but not MIMoSA. After adjusting for age, WHM-SynthSeg, nnU-Net FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1 had significant associations with EDSS and SNRS scores.

nnU-Net and PLAn performed best in segmenting WML on pULF-MRI at 64 mT, providing accurate quantitative estimates of WML burden. Moreover, WML volumes estimated by these algorithms were associated with clinical measures of disability, underscoring their utility for reflecting clinical and radiological disease severity. Given pULF-MRI’s mobility and lower cost, these findings highlight its relevance in clinical trials, particularly in involving more participants who face logistical constraints and barriers.

Reproduced under the paper's license (CC0), from the paper cited above.

Repository

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

MIC-DKFZ/nnUNet

License: Apache-2.0
State: the link answers, verified on 26 September 2026
Evidence: files inventoried
Commit: 202f6baa0adc2ef5f7b615df19cc4da970412cc0, 25 September 2026
Languages: Python (216), Shell (7)
Size: 303 files, 223 scripts
Software Heritage: archived
Found in: the text, “Automated Lesion Segmentation Methods”
Holds: README, license file, environment (pyproject.toml, setup.py), tests, continuous integration, documentation
Not found: CITATION.cff
Tools: nnU-Net (125 files), NumPy (73 files), PyTorch (56 files), SimpleITK (10 files), scikit-image (6 files), SciPy (5 files), NiBabel (4 files), pandas (4 files), tifffile (3 files), Matplotlib (2 files), scikit-learn (1 file), seaborn (1 file)
Availability: 1 check, the latest on 26 September 2026: the link answers
  • 26 September 2026: the link answers
225 files
At the source: github.com/MIC-DKFZ/nnUNet

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:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 223 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.

Availability of data and material

Supplemental data, including deidentified clinical data, deidentified imaging data, and deep-learning models (nnU-Net-FL, nnU-Net-FL/T1w, PLAn-FL, and PLAn-FL/T1w), will be released upon publication.

Reproduced under the paper's license (CC0), 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

  • Language: n/a → en
  • Authors: added Ashley A. Thommana (0009-0004-1117-8987); Corinne A. Donnay (0009-0006-4856-8422); Gina Norato (0000-0003-1324-0217); María I. Gaitán (0000-0003-4455-5641); Daniel S. Reich (0000-0002-2628-4334); removed Ashley A. Thommana; Corinne A. Donnay; Gina Norato; María I. Gaitán; Daniel S. Reich

Version 1, 27 September 2026: the first record

Recorded: type, journal, dates, 8 authors, 8 keywords, 1 funder, 34 references.

Cite

This paper

Thommana, A. A., Donnay, C. A., Norato, G., Gaitán, M. I., Griffanti, L., Nair, G., Reich, D. S., & Okar, S. V. (2026). Portable Ultra-Low Field MRI Deep-Learning Algorithms for White Matter Lesion Segmentation Improve Accuracy and Reflect Clinical Disability in Multiple Sclerosis. medRxiv (preprint). https://doi.org/10.64898/2026.07.15.26357954

BibTeX

@article{thommana2026portable,
author = {Thommana, Ashley A. and Donnay, Corinne A. and Norato, Gina and Gaitán, María I. and Griffanti, Ludovica and Nair, Govind and Reich, Daniel S. and Okar, Serhat V.},
title = {{Portable Ultra-Low Field MRI Deep-Learning Algorithms for White Matter Lesion Segmentation Improve Accuracy and Reflect Clinical Disability in Multiple Sclerosis}},
journal = {medRxiv (preprint)},
year = {2026},
month = jul,
publisher = {medRxiv},
doi = {10.64898/2026.07.15.26357954},
url = {https://doi.org/10.64898/2026.07.15.26357954}
}

RIS

TY - JOUR
AU - Thommana, Ashley A.
AU - Donnay, Corinne A.
AU - Norato, Gina
AU - Gaitán, María I.
AU - Griffanti, Ludovica
AU - Nair, Govind
AU - Reich, Daniel S.
AU - Okar, Serhat V.
TI - Portable Ultra-Low Field MRI Deep-Learning Algorithms for White Matter Lesion Segmentation Improve Accuracy and Reflect Clinical Disability in Multiple Sclerosis
T2 - medRxiv (preprint)
J2 - medRxiv
PY - 2026
DA - 2026/07/17
PB - medRxiv
DO - 10.64898/2026.07.15.26357954
UR - https://doi.org/10.64898/2026.07.15.26357954
LA - en
ER -

CSL-JSON

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

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