Contributors: Biomedical Imaging Group Rotterdam (BIG); Erasmus University Medical Center Rotterdam (Erasmus MC); Department of Cardiology; Department of Radiology; Department of Cardiology, UMC Utrecht; University Medical Center Utrecht (UMCU); Leiden University Medical Center Leiden (LUMC); Universiteit Leiden = Leiden University-Universiteit Leiden = Leiden University; Faculty of Engineering and Natural Sciences (Sabanci University); Sabanci University Istanbul; Computer Aided Medical Procedures & Augmented Reality (CAMPAR); Technische Universität Munchen = Technical University Munich = Université Technique de Munich (TUM); Grupo Takina; Pontificia universidad Javeriana, Cali; Department of Biomedical Engineering National Taiwan University (NTU); National Taiwan University (NTU); Advanced Digital and Signal Image Processing Group (ADSIP); University of Central Lancashire Preston (UCLAN); A3SI; Laboratoire d'Informatique Gaspard-Monge (LIGM); Université Paris-Est Marne-la-Vallée (UPEM)-École nationale des ponts et chaussées (ENPC)-ESIEE Paris-Fédération de Recherche Bézout (BEZOUT); Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Paris-Est Marne-la-Vallée (UPEM)-École nationale des ponts et chaussées (ENPC)-ESIEE Paris-Fédération de Recherche Bézout (BEZOUT); Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Recherche et de Développement de l'EPITA (LRDE); Ecole Pour l'Informatique et les Techniques Avancées (EPITA)-Ecole Pour l'Informatique et les Techniques Avancées (EPITA); Toshiba Medical Visualization Systems; Toshiba; Electrical and Electronics Engineering Istanbul; Bahcesehir University Istanbul; Center for Medical Imaging Science and Visualization - Dept. of Medical and Health Sciences; Linköping University (LIU); Biomedical Engineering Department; Columbia University New York; Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE); Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS); Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL); Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS); Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Equipe KEIA; Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS); Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S); Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S); Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UniCA); Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS); Biomedical Engineering Istanbul; Quantitative Imaging Group Imaging Science and Technology, Faculty of Applied Sciences; Delft University of Technology (TU Delft); Rcadia Medical Imaging; Rcadia Medical Imaging Ltd; VRVis Research Center for Virtual Reality and Visualization
Abstract: International audience ; 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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