Linked Life Data

20426189

Source:http://linkedlifedata.com/resource/pubmed/id/20426189

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
The feasibility of an automated calibration method for estimating the arterial input function when calculating pharmacokinetic parameters from Dynamic Contrast Enhanced MRI is shown. In a previous study, it was demonstrated that the computer aided diagnoses (CADx) system performs optimal when per patient calibration was used, but required manual annotation of reference tissue. In this study we propose a fully automated segmentation method that tackles this limitation and tested the method with our CADx system when discriminating prostate cancer from benign areas in the peripheral zone. A method was developed to automatically segment normal peripheral zone tissue (PZ). Context based segmentation using the Otsu histogram based threshold selection method and by Hessian based blob detection, was developed to automatically select PZ as reference tissue for the per patient calibration. In 38 consecutive patients carcinoma, benign and normal tissue were annotated on MR images by a radiologist and a researcher using whole mount step-section histopathology as standard of reference. A feature set comprising pharmacokinetic parameters was computed for each ROI and used to train a support vector machine (SVM) as classifier. In total 42 malignant, 29 benign and 37 normal regions were annotated. The diagnostic accuracy obtained for differentiating malignant from benign lesions using a conventional general patient plasma profile showed an accuracy of 0.65 (0.54-0.76). Using the automated segmentation per patient calibration method the diagnostic value improved to 0.80 (0.71-0.88), whereas the manual segmentation per patient calibration showed a diagnostic performance of 0.80 (0.70-0.90). These results show that an automated per-patient calibration is feasible, a significant better discriminating performance compared to the conventional fixed calibration was obtained and the diagnostic accuracy is similar to using manual per-patient calibration.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
12
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
836-43
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Automated calibration for computerized analysis of prostate lesions using pharmacokinetic magnetic resonance images.
pubmed:affiliation
Department of Radiology, University Medical Centre Nijmegen, The Netherlands. p.vos@rad.umcn.nl
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't