Spinal cord grey matter segmentation challenge
(2017) In NeuroImage 152. p.312-329- 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... (More)
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.
(Less)
- author
- Prados, Ferran
; Ashburner, John
; Blaiotta, Claudia
; Brosch, Tom
; Carballido-Gamio, Julio
; Cardoso, Manuel Jorge
; Conrad, Benjamin N
; Datta, Esha
; Dávid, Gergely
; Leener, Benjamin De
; Dupont, Sara M
; Freund, Patrick
; Wheeler-Kingshott, Claudia A M Gandini
; Grussu, Francesco
; Henry, Roland
; Landman, Bennett A
; Ljungberg, Emil
LU
; Lyttle, Bailey
; Ourselin, Sebastien
; Papinutto, Nico
; Saporito, Salvatore
; Schlaeger, Regina
; Smith, Seth A
; Summers, Paul
; Tam, Roger
; Yiannakas, Marios C
; Zhu, Alyssa
and Cohen-Adad, Julien
(Less) - publishing date
- 2017
- type
- Contribution to journal
- publication status
- published
- 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
- in
- NeuroImage
- volume
- 152
- pages
- 312 - 329
- publisher
- Elsevier
- external identifiers
-
- scopus:85014917868
- pmid:28286318
- ISSN
- 1095-9572
- DOI
- 10.1016/j.neuroimage.2017.03.010
- language
- English
- LU publication?
- no
- additional info
- Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.
- id
- c1ecdccb-2666-4031-a33b-4b644ea938d1
- date added to LUP
- 2022-04-05 11:11:08
- date last changed
- 2024-02-02 01:52:44
@article{c1ecdccb-2666-4031-a33b-4b644ea938d1,
abstract = {{<p>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.</p>}},
author = {{Prados, Ferran and Ashburner, John and Blaiotta, Claudia and Brosch, Tom and Carballido-Gamio, Julio and Cardoso, Manuel Jorge and Conrad, Benjamin N and Datta, Esha and Dávid, Gergely and Leener, Benjamin De and Dupont, Sara M and Freund, Patrick and Wheeler-Kingshott, Claudia A M Gandini and Grussu, Francesco and Henry, Roland and Landman, Bennett A and Ljungberg, Emil and Lyttle, Bailey and Ourselin, Sebastien and Papinutto, Nico and Saporito, Salvatore and Schlaeger, Regina and Smith, Seth A and Summers, Paul and Tam, Roger and Yiannakas, Marios C and Zhu, Alyssa and Cohen-Adad, Julien}},
issn = {{1095-9572}},
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}},
language = {{eng}},
pages = {{312--329}},
publisher = {{Elsevier}},
series = {{NeuroImage}},
title = {{Spinal cord grey matter segmentation challenge}},
url = {{http://dx.doi.org/10.1016/j.neuroimage.2017.03.010}},
doi = {{10.1016/j.neuroimage.2017.03.010}},
volume = {{152}},
year = {{2017}},
}