Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-2-17
pubmed:abstractText
A new semi-automatic method for segmenting the spinal cord from MR images is presented. The method is based on an active surface (AS) model of the cord surface, with intrinsic smoothness constraints. The model is initialized by the user marking the approximate cord center-line on a few representative slices, and the compact surface parametrization results in a rapid segmentation, taking on the order of 1 min. Using 3-D acquired T(1)-weighted images of the cervical spine from human controls and patients with multiple sclerosis, the intra- and inter-observer reproducibilities were evaluated, and compared favorably with an existing cord segmentation method. While the AS method overestimated the cord area by approximately 14% compared to manual outlining, correlations between cord cross-sectional area and clinical disability scores confirmed the relevance of the new method in measuring cord atrophy in multiple sclerosis. Segmentation of the cord from 2-D multi-slice T(2)-weighted images is also demonstrated over the cervical and thoracic region. Since the cord center-line is an intrinsic parameter extracted as part of the segmentation process, the image can be resampled such that the center-line forms one coordinate axis of a new image, allowing simple visualization of the cord structure and pathology; this could find wider application in standard radiological practice.
pubmed:grant
pubmed:commentsCorrections
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pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1095-9572
pubmed:author
pubmed:copyrightInfo
Copyright 2010 Elsevier Inc. All rights reserved.
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
50
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
446-55
pubmed:dateRevised
2011-7-27
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis.
pubmed:affiliation
Department of Cardiovascular Sciences, University of Leicester, Leicester Royal Infirmary, Leicester, UK. mah5@le.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural