Linked Life Data

17633705

Source:http://linkedlifedata.com/resource/pubmed/id/17633705

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-7-18
pubmed:abstractText
The segmentation of neonatal cortex from magnetic resonance (MR) images is much more challenging than the segmentation of cortex in adults. The main reason is the inverted contrast between grey matter (GM) and white matter (WM) that occurs when myelination is incomplete. This causes mislabeled partial volume voxels, especially at the interface between GM and cerebrospinal fluid (CSF). We propose a fully automatic cortical segmentation algorithm, detecting these mislabeled voxels using a knowledge-based approach and correcting errors by adjusting local priors to favor the correct classification. Our results show that the proposed algorithm corrects errors in the segmentation of both GM and WM compared to the classic EM scheme. The segmentation algorithm has been tested on 25 neonates with the gestational ages ranging from approximately 27 to 45 weeks. Quantitative comparison to the manual segmentation demonstrates good performance of the method (mean Dice similarity: 0.758 +/- 0.037 for GM and 0.794 +/- 0.078 for WM).
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
257-69
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Automatic cortical segmentation in the developing brain.
pubmed:affiliation
Imaging Sciences Department, Imperial College, London, Du cane Road, UK. hui.xue@imperial.ac.uk
pubmed:publicationType
Journal Article, Evaluation Studies