Linked Life Data

20425996

Source:http://linkedlifedata.com/resource/pubmed/id/20425996

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2010-4-29
pubmed:abstractText
Automatic segmentation of structures with missing or invisible borders is a challenging task. Since structures in the lungs are related, humans use contextual and shape information to infer the position of invisible borders. An example of a task in which the borders are often incomplete or invisible is the segmentation of the pulmonary lobes. In this paper, a fully automatic segmentation of the pulmonary lobes in chest CT scans is presented. The method is especially designed to be robust to incomplete fissures by incorporating contextual information from automatic lung, fissure, and bronchial tree segmentations, as well as shape information. Since the method relies on the result of automatic segmentations, it is important that the method is robust against failure of one or more of these segmentation methods. In an extensive experiment on 10 chest CT scans with manual segmentations, the robustness of the method to incomplete fissures and missing input segmentations is shown. In a second experiment on 100 chest CT scans with incomplete fissures, the method is shown to perform well.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
12
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
263-71
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Automatic segmentation of the pulmonary lobes from fissures, airways, and lung borders: evaluation of robustness against missing data.
pubmed:affiliation
Image Sciences Institute, University Medical Center, Utrecht, The Netherlands.
pubmed:publicationType
Journal Article, Evaluation Studies