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Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography.
Med. Image Anal. 17, 859-876 (2013)
Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Computed tomography angiography (CTA); Coronary arteries; Standardized evaluation framework; Stenoses detection; Stenoses quantification; American-heart-association ; Automatic Detection ; Cross-sections ; Data Sets ; Ct ; Disease ; Plaque ; Images
ISSN (print) / ISBN 1361-8415
Journal Medical Image Analysis
Quellenangaben Volume: 17, Issue: 8, Pages: 859-876
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)