Evidence map›Paper›PMID 28286318›Full record

ArticleNeuroImage2017

Spinal cord grey matter segmentation challenge.

Ferran Prados, John Ashburner, Claudia Blaiotta, Tom Brosch, Julio Carballido-Gamio, Manuel Jorge Cardoso, Benjamin N Conrad, Esha Datta, Gergely Dávid, Benjamin De Leener and 18 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NeuroImage, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers.

0numbers the graph read from it
0cells of the map it votes in
51citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

51 citing papers in PubMed.

  1. Review
  2. Brain and retina in Alzheimer's disease: Pathological intersections and estimates from imaging.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025
    Review
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  5. Article
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  7. Article
  8. Article
  9. Article
  10. Reproducible Spinal Cord Quantitative MRI Analysis with the Spinal Cord Toolbox.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024
    Review
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Automatic brain structure segmentation forQuantitative imaging in medicine and surgery · 2023
    Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

28 authors.

Ferran PradosTranslational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK. Electronic address: f.carrasco@ucl.ac.uk.
John AshburnerWellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
Claudia BlaiottaWellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.
Tom BroschDepartment of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4.
Julio Carballido-GamioDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA.
Manuel Jorge CardosoTranslational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK.
Benjamin N ConradDepartment of Electrical Engineering, Computer Science, Biomedical Engineering, Radiology and Radiological Sciences, Institute of Image Science at Vanderbilt University, Nashville, TN, USA.
Esha DattaDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA.
Gergely DávidSpinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Switzerland.
Benjamin De LeenerNeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada.
Sara M DupontNeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada.
Patrick FreundSpinal Cord Injury Center Balgrist, University Hospital Zurich, University of Zurich, Switzerland.
Claudia A M Gandini Wheeler-KingshottNMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK; Brain MRI 3T Centre, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Italy.
Francesco GrussuNMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK.
Roland HenryDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA.
Bennett A LandmanDepartment of Electrical Engineering, Computer Science, Biomedical Engineering, Radiology and Radiological Sciences, Institute of Image Science at Vanderbilt University, Nashville, TN, USA.
Emil LjungbergDepartment of Medicine, University of British Columbia, Vancouver, BC, Canada V6T 2B5.
Bailey LyttleInstitute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
Sebastien OurselinTranslational Imaging Group, Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, Malet Place Engineering Building, London WC1E 6BT, UK; Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, Queen Square, London WC1N 3BG, UK.
Nico PapinuttoDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA.
Salvatore SaporitoEindhoven University of Technology, Netherlands.
Regina SchlaegerDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA.
Seth A SmithDepartment of Radiology and Radiological Sciences, Biomedical Engineering, Ophthalmology, Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
Paul SummersDepartment of Radiology, European Institute of Oncology, University of Modena and Reggio Emilia, 41121, Modena, MO, Italy.
Roger TamDepartment of Radiology, UBC MS/MRI Research Group, University of British Columbia, Vancouver, BC, Canada V6T 2B5.
Marios C YiannakasNMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, University College London, Russell Square, London WC1B 5EH, UK.
Alyssa ZhuDepartment of Neurology, University of California San Francisco, San Francisco, CA, USA.
Julien Cohen-AdadNeuroPoly Lab, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada. Electronic address: jcohen@polymtl.ca.

Funding

Institutional Predoctoral Training Program in Biomedical Imaging ScienceT32EB014841 · NIBIB · VANDERBILT UNIVERSITY · PI GORE, JOHN C · 2013 to 2017
$771k
In Vivo Macromolecular and Protein-Based MRI in the Spinal Cord of MS PatientsR21NS087465 · NINDS · VANDERBILT UNIVERSITY · PI SMITH, SETH A · 2014 to 2015
$431k
Medical Research Council MR/J01107X/1NIBIB NIH HHS T32 EB014841NINDS NIH HHS R21 NS087465
6 · The paper itself

Abstract

An important image processing step in spinal cord magnetic resonance imaging is the ability to reliably and accurately segment grey and white matter for tissue specific analysis. There are several semi- or fully-automated segmentation methods for cervical cord cross-sectional area measurement with an excellent performance close or equal to the manual segmentation. However, grey matter segmentation is still challenging due to small cross-sectional size and shape, and active research is being conducted by several groups around the world in this field. Therefore a grey matter spinal cord segmentation challenge was organised to test different capabilities of various methods using the same multi-centre and multi-vendor dataset acquired with distinct 3D gradient-echo sequences. This challenge aimed to characterize the state-of-the-art in the field as well as identifying new opportunities for future improvements. Six different spinal cord grey matter segmentation methods developed independently by various research groups across the world and their performance were compared to manual segmentation outcomes, the present gold-standard. All algorithms provided good overall results for detecting the grey matter butterfly, albeit with variable performance in certain quality-of-segmentation metrics. The data have been made publicly available and the challenge web site remains open to new submissions. No modifications were introduced to any of the presented methods as a result of this challenge for the purposes of this publication.

Indexed as

AdultAlgorithmsBrain MappingCervical CordFemaleGray MatterHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedReproducibility of ResultsWhite MatterChallengeEvaluation metricsGrey matterMRISegmentationSpinal cord

Identifiers

PMID28286318
PMCPMC5440179

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.