Linked Life Data

19628453

Source:http://linkedlifedata.com/resource/pubmed/id/19628453

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
12
pubmed:dateCreated
2009-11-25
pubmed:abstractText
Lobe identification in computed tomography (CT) examinations is often an important consideration during the diagnostic process as well as during treatment planning because of their relative independence of each other in terms of anatomy and function. In this paper, we present a new automated scheme for segmenting lung lobes depicted on 3-D CT examinations. The unique characteristic of this scheme is the representation of fissures in the form of implicit functions using Radial Basis Functions (RBFs), capable of seamlessly interpolating "holes" in the detected fissures and smoothly extrapolating the fissure surfaces to the lung boundaries resulting in a "natural" segmentation of lung lobes. A previously developed statistically based approach is used to detect pulmonary fissures and the constraint points for implicit surface fitting are selected from detected fissure surfaces in a greedy manner to improve fitting efficiency. In a preliminary assessment study, lobe segmentation results of 65 chest CT examinations, five of which were reconstructed with three section thicknesses of 0.625 mm, 1.25 mm, and 2.5 mm, were subjectively and independently evaluated by two experienced chest radiologists using a five category rating scale (i.e., excellent, good, fair, poor, and unacceptable). Thirty-three of 65 examinations (50.8%) with a section thickness of 0.625 mm were rated as either "excellent" or "good" by both radiologists and only one case (1.5%) was rated by both radiologists as "poor" or "unacceptable." Comparable performance was obtained with a slice thickness of 1.25 mm, but substantial performance deterioration occurred in examinations with a section thickness of 2.5 mm. The advantages of this scheme are its full automation, relative insensitivity to fissure completeness, and ease of implementation.
pubmed:grant
pubmed:commentsCorrections
http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-10489181, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-11452059, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-11695768, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-15273615, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-15537839, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-15955862, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-16807062, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-18043363, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-18515044, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-8191982, http://linkedlifedata.com/resource/pubmed/commentcorrection/19628453-9641332
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1558-0062
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1986-96
pubmed:dateRevised
2011-6-7
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Pulmonary lobe segmentation in CT examinations using implicit surface fitting.
pubmed:affiliation
Imaging Research Division, Department of Radiology, University of Pittsburgh, Pittsburgh, PA 15213, USA. puj@upmc.edu
pubmed:publicationType
Journal Article, Research Support, N.I.H., Extramural