Jelmer M Wolterink1, Tim Leiner2, Bob D de Vos1, Jean-Louis Coatrieux3, B Michael Kelm4, Satoshi Kondo5, Rodrigo A Salgado6, Rahil Shahzad7, Huazhong Shu8, Miranda Snoeren9, Richard A P Takx2, Lucas J van Vliet10, Theo van Walsum11, Tineke P Willems12, Guanyu Yang13, Yefeng Zheng14, Max A Viergever1, Ivana Išgum1. 1. Image Sciences Institute, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands. 2. Department of Radiology, University Medical Center Utrecht, Utrecht 3508 GA, The Netherlands. 3. INSERM, U1099, Rennes F-35000, France; LTSI, Université de Rennes 1, Rennes F-35000, France; and Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096, China. 4. Imaging and Computer Vision, Corporate Technology, Siemens AG, Erlangen 91051, Germany. 5. Konica Minolta, Inc., Osaka 569-8503, Japan. 6. Department of Radiology, University Hospital Antwerpen, Edegem 2650, Belgium. 7. Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden 2300 RC, The Netherlands; Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam 3000 CA, The Netherlands; and Quantitative Imaging Group, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft 2600 GA, The Netherlands. 8. Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096, China and Lab of Image Science and Technology, School of Computer Science and Technology, Nanjing 210096, China. 9. Department of Radiology, Radboud University Medical Center, Nijmegen 6500 HB, The Netherlands. 10. Quantitative Imaging Group, Department of Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft 2600 GA, The Netherlands. 11. Biomedical Imaging Group Rotterdam, Departments of Radiology and Medical Informatics, Erasmus MC, Rotterdam 3000 CA, The Netherlands. 12. Department of Radiology, University Medical Center Groningen, Groningen 9700 RB, The Netherlands. 13. Lab of Image Science and Technology, School of Computer Science and Technology, Nanjing 210096, China and Centre de Recherche en Information Biomédicale Sino-Français (LIA CRIBs), Nanjing 210096, China. 14. Imaging and Computer Vision, Corporate Technology, Siemens Corporation, Princeton, New Jersey 08540-6632.
Abstract
PURPOSE: The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. METHODS: Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. RESULTS: Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00. CONCLUSIONS: A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.
PURPOSE: The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular disease (CVD) events. In clinical practice, CAC is manually identified and automatically quantified in cardiac CT using commercially available software. This is a tedious and time-consuming process in large-scale studies. Therefore, a number of automatic methods that require no interaction and semiautomatic methods that require very limited interaction for the identification of CAC in cardiac CT have been proposed. Thus far, a comparison of their performance has been lacking. The objective of this study was to perform an independent evaluation of (semi)automatic methods for CAC scoring in cardiac CT using a publicly available standardized framework. METHODS: Cardiac CT exams of 72 patients distributed over four CVD risk categories were provided for (semi)automatic CAC scoring. Each exam consisted of a noncontrast-enhanced calcium scoring CT (CSCT) and a corresponding coronary CT angiography (CCTA) scan. The exams were acquired in four different hospitals using state-of-the-art equipment from four major CT scanner vendors. The data were divided into 32 training exams and 40 test exams. A reference standard for CAC in CSCT was defined by consensus of two experts following a clinical protocol. The framework organizers evaluated the performance of (semi)automatic methods on test CSCT scans, per lesion, artery, and patient. RESULTS: Five (semi)automatic methods were evaluated. Four methods used both CSCT and CCTA to identify CAC, and one method used only CSCT. The evaluated methods correctly detected between 52% and 94% of CAC lesions with positive predictive values between 65% and 96%. Lesions in distal coronary arteries were most commonly missed and aortic calcifications close to the coronary ostia were the most common false positive errors. The majority (between 88% and 98%) of correctly identified CAC lesions were assigned to the correct artery. Linearly weighted Cohen's kappa for patient CVD risk categorization by the evaluated methods ranged from 0.80 to 1.00. CONCLUSIONS: A publicly available standardized framework for the evaluation of (semi)automatic methods for CAC identification in cardiac CT is described. An evaluation of five (semi)automatic methods within this framework shows that automatic per patient CVD risk categorization is feasible. CAC lesions at ambiguous locations such as the coronary ostia remain challenging, but their detection had limited impact on CVD risk determination.
Authors: Hugo J Kuijf; J Matthijs Biesbroek; Jeroen De Bresser; Rutger Heinen; Simon Andermatt; Mariana Bento; Matt Berseth; Mikhail Belyaev; M Jorge Cardoso; Adria Casamitjana; D Louis Collins; Mahsa Dadar; Achilleas Georgiou; Mohsen Ghafoorian; Dakai Jin; April Khademi; Jesse Knight; Hongwei Li; Xavier Llado; Miguel Luna; Qaiser Mahmood; Richard McKinley; Alireza Mehrtash; Sebastien Ourselin; Bo-Yong Park; Hyunjin Park; Sang Hyun Park; Simon Pezold; Elodie Puybareau; Leticia Rittner; Carole H Sudre; Sergi Valverde; Veronica Vilaplana; Roland Wiest; Yongchao Xu; Ziyue Xu; Guodong Zeng; Jianguo Zhang; Guoyan Zheng; Christopher Chen; Wiesje van der Flier; Frederik Barkhof; Max A Viergever; Geert Jan Biessels Journal: IEEE Trans Med Imaging Date: 2019-03-19 Impact factor: 10.048
Authors: Riemer H J A Slart; Michelle C Williams; Luis Eduardo Juarez-Orozco; Christoph Rischpler; Marc R Dweck; Andor W J M Glaudemans; Alessia Gimelli; Panagiotis Georgoulias; Olivier Gheysens; Oliver Gaemperli; Gilbert Habib; Roland Hustinx; Bernard Cosyns; Hein J Verberne; Fabien Hyafil; Paola A Erba; Mark Lubberink; Piotr Slomka; Ivana Išgum; Dimitris Visvikis; Márton Kolossváry; Antti Saraste Journal: Eur J Nucl Med Mol Imaging Date: 2021-04-17 Impact factor: 9.236
Authors: Chris Boyd; Greg Brown; Timothy Kleinig; Joseph Dawson; Mark D McDonnell; Mark Jenkinson; Eva Bezak Journal: Diagnostics (Basel) Date: 2021-03-19