Linked Life Data

21335864

Source:http://linkedlifedata.com/resource/pubmed/id/21335864

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2011-2-21
pubmed:abstractText
Training in interventional radiology is slowly shifting towards simulation which allows the repetition of many interventions without putting the patient at risk. Accurate segmentation of anatomical structures is a prerequisite of realistic surgical simulation. Therefore, our aim is to develop a generic approach to provide fast and precise segmentation of various virtual anatomies covering a wide range of pathology, directly from patient CT/MRA images. This paper presents a segmentation framework including two segmentation methods: region model based level set segmentation and hierarchical segmentation. We compare them to an open source application ITK-SNAP which provides similar approaches. The subjective human influence such as inconsistent inter-observer errors and aliasing artifacts etc. are analysed. The proposed segmentation techniques have been successfully applied to create a database of various anatomies with different pathologies, which is used in computer-based simulation for interventional radiology training.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
T
pubmed:status
MEDLINE
pubmed:issn
0926-9630
pubmed:author
pubmed:issnType
Print
pubmed:volume
163
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
599-605
pubmed:meshHeading
pubmed:year
2011
pubmed:articleTitle
Segmentation of 3D vasculatures for interventional radiology simulation.
pubmed:affiliation
School of Computing, University of Leeds, UK. y.song@leeds.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't