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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/.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
Altmetric
4.087
2.931
99
129
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern
Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
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
Sprache
englisch
Veröffentlichungsjahr
2013
HGF-Berichtsjahr
2013
ISSN (print) / ISBN
1361-8415
e-ISSN
1361-8415
Zeitschrift
Medical Image Analysis
Quellenangaben
Band: 17,
Heft: 8,
Seiten: 859-876
Verlag
Elsevier
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-503800-001
WOS ID
WOS:000326662000001
Scopus ID
84880166505
Erfassungsdatum
2013-07-29