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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 David, Benjamin De Leener, Sara M. Dupont, Patrick Freund, Claudia Angela M. Gandini Wheeler-Kingshott, Francesco Grussu, Roland Henry, Bennett A. Landman, Emil Ljungberg, Bailey Lyttle, Sebastien Ourselin, Nico Papinutto, Salvatore Saporito, Regina Schlaeger, Seth A. Smith, Paul Summers, Roger Tam, Marios C. Yiannakas, Alyssa Zhu and Julien Cohen-Adad

Article (2017)

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Terms of Use: Creative Commons Attribution .
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Cite this document: Prados, F., Ashburner, J., Blaiotta, C., Brosch, T., Carballido-Gamio, J., Cardoso, M. J., ... Cohen-Adad, J. (2017). Spinal cord grey matter segmentation challenge. NeuroImage, 152, p. 312-329. doi:10.1016/j.neuroimage.2017.03.010
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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.

Uncontrolled Keywords

Adult; Algorithms; Brain Mapping/methods; Cervical Cord/anatomy & histology; Female; Gray Matter/anatomy & histology; Humans; Image Processing, Computer-Assisted/methods; Magnetic Resonance Imaging; Male; Middle Aged; Reproducibility of Results; White Matter/anatomy & histology; Challenge; Evaluation metrics; Grey matter; MRI; Segmentation; Spinal cord

Open Access document in PolyPublie
Subjects: 1900 Génie biomédical > 1900 Génie biomédical
1900 Génie biomédical > 1901 Technologie biomédicale
9000 Sciences de la santé > 9000 Sciences de la santé
Department: Département de génie informatique et génie logiciel
Research Center: NeuroPoly - Laboratoire de Recherche en Neuroimagerie
Date Deposited: 19 Apr 2022 13:36
Last Modified: 18 May 2022 10:29
PolyPublie URL: https://publications.polymtl.ca/5095/
Document issued by the official publisher
Journal Title: NeuroImage (vol. 152)
Publisher: Elsevier
Official URL: https://doi.org/10.1016/j.neuroimage.2017.03.010


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