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Reliability of a convolutional neural network in segmenting multiple sclerosis lesions from MRI: Impact of data augmentation, image modality and tolerance with U-Net architecture.

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Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 34 lines · 1.2 KB · CC-BY-4.0

  1. import nibabel as nib
  2. import numpy as np
  3. import os
  4. from scipy.ndimage import binary_closing, binary_dilation, binary_erosion, label, find_objects
  5. direc = 'Training_Labels/'
  6. numberoffiles = len(os.listdir(direc))
  7. print(numberoffiles)
  8. data_dir = 'NewDatasets/Training_Labels/Q1'
  10. # Load the binary NIfTI file
  11. for filename in os.listdir(direc):
  12. img = os.path.join(direc, filename)
  13. img = nib.load(img)
  14. data = img.get_fdata()
  16. # Threshold the image
  17. data_thresh = data < 1 # Holes are represented by 0s
  19. # Use connected component labeling to identify the holes
  20. labels, num_labels = label(data_thresh)
  22. # Calculate the volume of each hole
  23. for i in range(1, num_labels + 1):
  24. mask = labels == i
  25. volume = np.sum(mask)
  26. if volume > 13.5:
  27. # Fill the hole using a binary closing operation
  28. struct = np.ones((5, 5, 5)) # Use a 5x5x5 structuring element
  29. mask_closed = binary_closing(mask, structure=struct)
  30. data[mask_closed] = 1
  32. # Save the filled image as a new binary NIfTI file
  33. img_filled = nib.Nifti1Image(data.astype(np.uint8), img.affine, img.header)
  34. nib.save(img_filled, os.path.join(data_dir, filename))

FillholesnewUpload.py, under CC-BY-4.0 · at the source

Overview

Authors: Adam C Szekely-Kohn1,2, Marco Castellani1, Luca Baronti1,2, Zubair Ahmed3,4, William G K Manifold5, Michael Douglas3,6,7, Daniel M Espino1
ORCID iDs: Adam C Szekely-Kohn, Marco Castellani, Luca Baronti, Zubair Ahmed
  1. School of Engineering, University of Birmingham, Edgbaston, Birmingham, United Kingdom
  2. School of Computer Science, University of Birmingham, Edgbaston, Birmingham, United Kingdom
  3. Institute of Inflammation and Ageing, University of Birmingham, Edgbaston, Birmingham, United Kingdom
  4. University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, United Kingdom
  5. Royal North Shore Hospital, St Leonards, Sydney, New South Wales, Australia
  6. School of Neurology, Dudley Group NHS Foundation Trust, Russells Hall Hospital, Birmingham, United Kingdom
  7. School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
Institutions: University of Birmingham (United Kingdom); University Hospitals Birmingham NHS Foundation Trust (United Kingdom); Royal North Shore Hospital (Australia); Aston University (United Kingdom); Russells Hall Hospital (United Kingdom); Dudley Group NHS Foundation Trust (United Kingdom)
Journal: PLOS digital health, volume 5, issue 4, article e0001316
Dates: received 7 February 2025; accepted 4 March 2026; published online 1 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1371/journal.pdig.0001316 · PMID 41920815 · PMCID PMC13042652 · OpenAlex W7147083885
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), multiple sclerosis (population), methods / tools (subfield)
Methods: Connectivity, Statistics, Machine learning
Topic: Multiple Sclerosis Research Studies (Pathology and Forensic Medicine, Medicine), according to OpenAlex
Funding: Engineering and Physical Sciences Research Council (EP/T517926/1)
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

Multiple Sclerosis (MS) is an inflammatory demyelinating disease of the central nervous system, typically exhibiting radiologically identifiable lesions within the brain and spinal cord, features key to both diagnosis and clinical disease monitoring. Manually identifying and segmenting lesions is both difficult and time consuming, thus optimising approaches to reliably automate segmentation is highly beneficial. The aim of this study was to assess the impact of data augmentation and manipulation on the accuracy of automated lesion segmentation using MRI scans from MS patients. Factors examined include MRI modalities in both isolation and combination, image augmentation, lesion size and size of testing set relative to training. The MICCAI 2016 MS dataset was used in this study, with U-Net chosen as the algorithmic method for segmentation. Each factor was optimised and then combined to maximise segmentation accuracy; the Dice metric was used as the focal metric to assess the efficacy of any given permutation of the setup. Statistical significance was assessed using the Mann–Whitney U-test, with each permutation repeated five to ten times to ensure robustness. The best Dice score achieved using the testing and training dataset as outlined in the MICCAI 2016 challenge rubric was 0.59, approximately a 2% improvement against controls. To achieve this result whilst adhering to the training and testing distribution as defined in the dataset publication, the optimal imaging sequence was determined to be proton density. The augmentation conditions used included implementing an additional rotation of the dataset (doubling it in size) and excluding lesions < 36.43 mm3 in volume. The impact of data manipulation and augmentation was found to be statistically significant against controls using a Mann-Whitney U-test for lesion segmentation. An ancillary finding of this study was that there was no statistically significant difference between using one MRI modality for training and another for testing.

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

Repositories

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

Zenodo 16211828

License: CC-BY-4.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: “Data Availability”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: NumPy (6 files), MONAI (5 files), PyTorch (5 files), Matplotlib (3 files), NiBabel (3 files), SciPy (3 files), scikit-image (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
  • 28 September 2026: the link answers (HTTP 200)
9 files
At the source: doi.org/10.5281/zenodo.16211828

adamhillingerszekely/unetbrainms

License: none: the authors keep all their rights
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: dcd9e39a693ce18df4059617e189cc95c1fbb63e, 5 May 2026
Languages: Python (8)
Size: 16 files, 8 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, environment (MSLesionSegmentation/requirements.txt)
Not found: license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (6 files), MONAI (5 files), PyTorch (5 files), Matplotlib (3 files), NiBabel (3 files), SciPy (3 files), scikit-image (1 file), scikit-learn (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
9 files
At the source: github.com/AdamHillingerSzekely/UnetBrainMS/tree/MSsegBenchMark

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;
  • 16 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

Data used can be found in publicly accessible data repositories: (Shanoir), https://shanoir.irisa.fr/shanoir-ng/welcome The data used is already published under the name of ‘MS lesions segmentation challenge of MICCAI 2016’. Therefore ethical approval was not required for this study. The code used can be found: doi.org/10.5281/zenodo.16211828.

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, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 1 funder, 37 references.

Cite

This paper

Szekely-Kohn, A. C., Castellani, M., Baronti, L., Ahmed, Z., Manifold, W. G. K., Douglas, M., & Espino, D. M. (2026). Reliability of a convolutional neural network in segmenting multiple sclerosis lesions from MRI: Impact of data augmentation, image modality and tolerance with U-Net architecture. PLOS digital health, 5(4), e0001316. https://doi.org/10.1371/journal.pdig.0001316

BibTeX

@article{szekelykohn2026reliability,
author = {Szekely-Kohn, Adam C and Castellani, Marco and Baronti, Luca and Ahmed, Zubair and Manifold, William G K and Douglas, Michael and Espino, Daniel M},
title = {{Reliability of a convolutional neural network in segmenting multiple sclerosis lesions from MRI: Impact of data augmentation, image modality and tolerance with U-Net architecture}},
journal = {PLOS digital health},
year = {2026},
month = apr,
volume = {5},
number = {4},
pages = {e0001316},
publisher = {PLOS},
issn = {2767-3170},
doi = {10.1371/journal.pdig.0001316},
url = {https://doi.org/10.1371/journal.pdig.0001316},
pmid = {41920815},
pmcid = {PMC13042652}
}

RIS

TY - JOUR
AU - Szekely-Kohn, Adam C
AU - Castellani, Marco
AU - Baronti, Luca
AU - Ahmed, Zubair
AU - Manifold, William G K
AU - Douglas, Michael
AU - Espino, Daniel M
TI - Reliability of a convolutional neural network in segmenting multiple sclerosis lesions from MRI: Impact of data augmentation, image modality and tolerance with U-Net architecture
T2 - PLOS digital health
J2 - PLOS Digit Health
PY - 2026
DA - 2026/04/01
VL - 5
IS - 4
SP - e0001316
SN - 2767-3170
PB - PLOS
DO - 10.1371/journal.pdig.0001316
UR - https://doi.org/10.1371/journal.pdig.0001316
LA - en
ER -

CSL-JSON

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"id": "10.1371/journal.pdig.0001316",
"type": "article-journal",
"title": "Reliability of a convolutional neural network in segmenting multiple sclerosis lesions from MRI: Impact of data augmentation, image modality and tolerance with U-Net architecture",
"container-title": "PLOS digital health",
"author": [
{
"family": "Szekely-Kohn",
"given": "Adam C"
},
{
"family": "Castellani",
"given": "Marco"
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{
"family": "Baronti",
"given": "Luca"
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{
"family": "Ahmed",
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{
"family": "Manifold",
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"family": "Douglas",
"given": "Michael"
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{
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}
],
"container-title-short": "PLOS Digit Health",
"volume": "5",
"issue": "4",
"page": "e0001316",
"DOI": "10.1371/journal.pdig.0001316",
"PMID": "41920815",
"PMCID": "PMC13042652",
"ISSN": "2767-3170",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pdig.0001316",
"language": "en",
"issued": {
"date-parts": [
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2026,
4,
1
]
]
}
}

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

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