Linked Life Data

20426155

Source:http://linkedlifedata.com/resource/pubmed/id/20426155

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 2
pubmed:dateCreated
2010-4-29
pubmed:abstractText
We present a robust and accurate atlas-based brain segmentation method which uses multiple initial structure segmentations to simultaneously drive the image registration and achieve anatomically constrained correspondence. We also derive segmentation confidence maps (SCMs) from a given manually segmented training set; these characterize the accuracy of a given set of segmentations as compared to manual segmentations. We incorporate these in our cost term to weight the influence of initial segmentations in the multi-structure registration, such that low confidence regions are given lower weight in the registration. To account for correspondence errors in the underlying registration, we use a supervised atlas correction technique and present a method for correcting the atlas segmentation to account for possible errors in the underlying registration. We applied our multi-structure atlas-based segmentation and supervised atlas correction to segment the amygdala in a set of 23 autistic patients and controls using leave-one-out cross validation, achieving a Dice overlap score of 0.84. We also applied our method to eight subcortical structures in MRI from the Internet Brain Segmentation Repository, with results better or comparable to competing methods.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
12
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
549-57
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Robust atlas-based brain segmentation using multi-structure confidence-weighted registration.
pubmed:affiliation
School of Engineering Science, Simon Fraser University, 8888 University Drive, Burnaby BC, V5A 1S6, Canada. akhanf@sfu.ca
pubmed:publicationType
Journal Article