| pubmed-article:21335864 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:21335864 | lifeskim:mentions | umls-concept:C0005839 | lld:lifeskim |
| pubmed-article:21335864 | lifeskim:mentions | umls-concept:C0441635 | lld:lifeskim |
| pubmed-article:21335864 | lifeskim:mentions | umls-concept:C0034602 | lld:lifeskim |
| pubmed-article:21335864 | lifeskim:mentions | umls-concept:C0679083 | lld:lifeskim |
| pubmed-article:21335864 | pubmed:dateCreated | 2011-2-21 | lld:pubmed |
| pubmed-article:21335864 | 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. | lld:pubmed |
| pubmed-article:21335864 | pubmed:language | eng | lld:pubmed |
| pubmed-article:21335864 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:21335864 | pubmed:citationSubset | T | lld:pubmed |
| pubmed-article:21335864 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:21335864 | pubmed:issn | 0926-9630 | lld:pubmed |
| pubmed-article:21335864 | pubmed:author | pubmed-author:GouldDerekD | lld:pubmed |
| pubmed-article:21335864 | pubmed:author | pubmed-author:BelloFernando... | lld:pubmed |
| pubmed-article:21335864 | pubmed:author | pubmed-author:SongYiY | lld:pubmed |
| pubmed-article:21335864 | pubmed:author | pubmed-author:LubozVincentV | lld:pubmed |
| pubmed-article:21335864 | pubmed:author | pubmed-author:KingDanielD | lld:pubmed |
| pubmed-article:21335864 | pubmed:author | pubmed-author:BulpittAndyA | lld:pubmed |
| pubmed-article:21335864 | pubmed:author | pubmed-author:DinNizarN | lld:pubmed |
| pubmed-article:21335864 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:21335864 | pubmed:volume | 163 | lld:pubmed |
| pubmed-article:21335864 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:21335864 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:21335864 | pubmed:pagination | 599-605 | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:meshHeading | pubmed-meshheading:21335864... | lld:pubmed |
| pubmed-article:21335864 | pubmed:year | 2011 | lld:pubmed |
| pubmed-article:21335864 | pubmed:articleTitle | Segmentation of 3D vasculatures for interventional radiology simulation. | lld:pubmed |
| pubmed-article:21335864 | pubmed:affiliation | School of Computing, University of Leeds, UK. y.song@leeds.ac.uk | lld:pubmed |
| pubmed-article:21335864 | pubmed:publicationType | Journal Article | lld:pubmed |
| pubmed-article:21335864 | pubmed:publicationType | Research Support, Non-U.S. Gov't | lld:pubmed |
