Document Detail


Carotid stenosis assessment with multi-detector CT angiography: comparison between manual and automatic segmentation methods.
MedLine Citation:
PMID:  23135615     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Luminal stenosis is used for selecting the optimal management strategy for patients with carotid artery disease. The aim of this study is to evaluate the reproducibility of carotid stenosis quantification using manual and automated segmentation methods using submillimeter through-plane resolution Multi-Detector CT angiography (MDCTA). 35 patients having carotid artery disease with >30 % luminal stenosis as identified by carotid duplex imaging underwent contrast enhanced MDCTA. Two experienced CT readers quantified carotid stenosis from axial source images, reconstructed maximum intensity projection (MIP) and 3D-carotid geometry which was automatically segmented by an open-source toolkit (Vascular Modelling Toolkit, VMTK) using NASCET criteria. Good agreement among the measurement using axial images, MIP and automatic segmentation was observed. Automatic segmentation methods show better inter-observer agreement between the readers (intra-class correlation coefficient (ICC): 0.99 for diameter stenosis measurement) than manual measurement of axial (ICC = 0.82) and MIP (ICC = 0.86) images. Carotid stenosis quantification using an automatic segmentation method has higher reproducibility compared with manual methods.
Authors:
Chengcheng Zhu; Andrew J Patterson; Owen M Thomas; Umar Sadat; Martin J Graves; Jonathan H Gillard
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-11-8
Journal Detail:
Title:  The international journal of cardiovascular imaging     Volume:  -     ISSN:  1875-8312     ISO Abbreviation:  Int J Cardiovasc Imaging     Publication Date:  2012 Nov 
Date Detail:
Created Date:  2012-11-8     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100969716     Medline TA:  Int J Cardiovasc Imaging     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Radiology, Cambridge University Hospitals Foundation Trust, University of Cambridge, Box 218, Cambridge, CB2 0QQ, UK.
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