Linked Life Data

20879382

Source:http://linkedlifedata.com/resource/pubmed/id/20879382

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 3
pubmed:dateCreated
2010-9-30
pubmed:abstractText
In this paper we propose a novel and robust system for the automated identification of major sulci on cortical surfaces. Using multiscale representation and intrinsic surface mapping, our system encodes anatomical priors in manually traced sulcal lines with an intrinsic atlas of major sulci. This allows the computation of both individual and joint likelihood of sulcal lines for their automatic identification on cortical surfaces. By modeling sulcal anatomy with intrinsic geometry, our system is invariant to pose differences and robust across populations and surface extraction methods. In our experiments, we present quantitative validations on twelve major sulci to show the excellent agreement of our results with manually traced curves. We also demonstrate the robustness of our system by successfully applying an atlas of Chinese population to identify sulci on Caucasian brains of different age groups, and surfaces extracted by three popular software tools.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
13
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
49-56
pubmed:dateRevised
2011-9-26
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Automated sulci identification via intrinsic modeling of cortical anatomy.
pubmed:affiliation
Lab of Neuro Imaging, UCLA School of Medicine, Los Angeles, CA, USA. yshi@loni.ucla.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural