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Cited by 134Cardiac Imaging and DiagnosticsMedical Image Segmentation TechniquesRadiomics and Machine Learning in Medical Imaging

Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT

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de Vos, Bob D., Wolterink, Jelmer M., Leiner, Tim, de Jong, Pim A., Lessmann, Nikolas, Isgum, Ivana·2019·IEEE Transactions on Medical Imaging
DOI 10.1109/tmi.2019.2899534
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Abstract

Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet the demands of the increasing interest in quantification of CAC, i.e., coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed. The current automatic calcium scoring methods are relatively computationally expensive and only provide scores for one type of CT. To address this, we propose a computationally efficient method that employs two convolutional neural networks: the first performs registration to align the fields of view of input CTs and the second performs direct regression of the calcium score, thereby circumventing time-consuming intermediate CAC segmentation. Optional decision feedback provides insight into the regions that are contributed to the calcium score. Experiments were performed using 903 cardiac CT and 1687 chest CT scans. The method predicted calcium scores in less than 0.3 s. The intra-class correlation coefficient between predicted and manual calcium scores was 0.98 for both cardiac and chest CT. The method showed almost perfect agreement between automatic and manual CVD risk categorization in both the datasets, with a linearly weighted Cohen's kappa of 0.95 in cardiac CT and 0.93 in chest CT. Performance is similar to that of the state-of-the-art methods, but the proposed method is hundreds of times faster. By providing visual feedback, insight is given in the decision process, making it readily implementable in clinical and research settings.

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  • Other studies that investigated deep learning algorithms for automated coronary calcium quantification used smaller cohorts or proprietary technologies.

    “Although other studies have investigated deep learning algorithms for automated coronary calcium quantification34–42, they used smaller cohorts or proprietary technologies.”

    From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [36]

    The Introduction (pp. 1–2) describes this paper’s use of deep learning for automatic coronary calcium scoring. The Data section (pp. 2–3) reports its cohort sizes, but our copy does not establish that they were smaller than the citing study’s cohort or that the method used proprietary technology. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • A combined solution capable of analyzing cardiac and non-gated chest CT as presented in our investigation has only been proposed by de Vos et al. and van Velzen et al.

    “A combined solution capable of analyzing cardiac and non-gated chest CT as presented in our investigation has only been proposed by de Vos et al.36 and van Velzen et al.41.”

    From Deep convolutional neural networks to predict cardiovascular risk from computed tomography (2021) · cites this paper as [36]

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  1. [1][1] GBD 2015 Mortality and Causes of Death Collaborators, 'Global, regional, and national life expectancy, all-cause mortality, and causespecific mortality for 249 causes of death, 1980-2015: a systematic analysis for the global burden of disease study 2015,' Lancet , vol. 388, no. 10053, pp. 1459-1544, Oct 2016. -
  2. [3][3] J. Yeboah, R. McClelland, T. Polonsky, and et al., 'Comparison of novel risk markers for improvement in cardiovascular risk assessment in intermediate-risk individuals,' JAMA , vol. 308, no. 8, pp. 788-795, 2012. -
  3. [4][4] H. S. Hecht, 'Coronary artery calcium scanning: Past, present, and future,' JACC: Cardiovascular Imaging , vol. 8, no. 5, pp. 579 - 596, 2015. -
  4. [6][6] A. J. Einstein, L. L. Johnson, S. Bokhari, J. Son, R. C. Thompson, T. M. Bateman, S. W. Hayes, and D. S. Berman, 'Agreement of visual estimation of coronary artery calcium from low-dose ct attenuation correction scans in hybrid PET/CT and SPECT/CT with standard agatston score,' Journal of the American College of Cardiology , vol. 56, no. 23, pp. 1914-1921, Nov 2010. -
  5. [7][7] I. Mylonas, M. Kazmi, L. Fuller, R. A. deKemp, Y. Yam, L. Chen, R. S. Beanlands, and B. J. W. Chow, 'Measuring coronary artery calcification using positron emission tomography-computed tomography attenuation correction images,' European Heart Journal Cardiovascular Imaging , vol. 13, no. 9, pp. 786-792, Sep 2012. -
  6. [8][8] S. A. M. Gernaat, I. Iˇ sgum, B. D. de Vos, R. A. P. Takx, D. A. YoungAfat, N. Rijnberg, D. E. Grobbee, Y. van der Graaf, P. A. de Jong, T. Leiner, and et al., 'Automatic coronary artery calcium scoring on radiotherapy planning CT scans of breast cancer patients: Reproducibility and association with traditional cardiovascular risk factors,' PLOS ONE , vol. 11, no. 12, p. e0167925, Dec 2016. -
  7. [9][9] P. C. Jacobs, M. Prokop, Y. van der Graaf, M. J. Gondrie, K. J. Janssen, H. J. de Koning, I. Iˇ sgum, R. J. van Klaveren, M. Oudkerk, B. van Ginneken, and W. P. Mali, 'Comparing coronary artery calcium and thoracic aorta calcium for prediction of all-cause mortality and cardiovascular events on low-dose non-gated computed tomography in a high-risk population of heavy smokers.' Atherosclerosis , vol. 209, no. 2, pp. 455-462, 2010. -
  8. [10][10] C. Chiles, F. Duan, G. W. Gladish, J. G. Ravenel, S. G. Baginski, B. S. Snyder, S. DeMello, S. S. Desjardins, R. F. Munden, and NLST Study Team, 'Association of coronary artery calcification and mortality in the national lung screening trial: A comparison of three scoring methods,' Radiology , vol. 276, no. 1, pp. 82-90, 2015. -
  9. [12][12] A. S. Agatston, W. R. Janowitz, F. J. Hildner, N. R. Zusmer, M. Viamonte, and R. Detrano, 'Quantification of coronary artery calcium using ultrafast computed tomography,' Journal of the American College of Cardiology , vol. 15, no. 4, pp. 827-832, 1990. -
  10. [13][13] J. A. Rumberger, B. H. Brundage, D. J. Rader, and G. Kondos, 'Electron beam computed tomographic coronary calcium scanning: A review and guidelines for use in asymptomatic persons,' Mayo Clinic Proceedings , vol. 74, no. 3, pp. 243-252, Mar 1999. -
  11. [14][14] J. Shemesh, C. I. Henschke, D. Shaham, R. Yip, A. O. Farooqi, M. D. Cham, D. I. McCauley, M. Chen, J. P. Smith, D. M. Libby, and et al., 'Ordinal scoring of coronary artery calcifications on low-dose CT scans of the chest is predictive of death from cardiovascular disease,' Radiology , vol. 257, no. 2, pp. 541-548, Nov 2010. -
  12. [15][15] G. Gonz´ alez, G. R. Washko, and R. S. J. Est´ epar, 'Automated agatston score computation in a large dataset of non ECG-gated chest computed tomography,' in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) , Apr 2016, pp. 53-57. -
  13. [16][16] Y. Xie, S. Liu, A. Miller, J. A. Miller, S. Markowitz, A. Akhund, and A. P. Reeves, 'Coronary artery calcification identification and labeling in low-dose chest CT images,' in Proceedings of SPIE , vol. 10134, 2017, pp. 10 134 - 10 134 - 8. -
  14. [17][17] I. Iˇ sgum, M. Prokop, M. Niemeijer, M. A. Viergever, and B. van Ginneken, 'Automatic coronary calcium scoring in low-dose chest computed tomography,' IEEE Transactions on Medical Imaging , vol. 31, no. 12, pp. 2322-2334, 2012. -
  15. [18][18] R. Shahzad, T. van Walsum, M. Schaap, A. Rossi, S. Klein, A. C. Weustink, P. J. de Feyter, L. J. van Vliet, and W. J. Niessen, 'Vessel specific coronary artery calcium scoring: an automatic system,' Academic Radiology , vol. 20, no. 1, pp. 1-9, Jan 2013. -
  16. [19][19] J. M. Wolterink, T. Leiner, R. A. P. Takx, M. A. Viergever, and I. Iˇ sgum, 'Automatic coronary calcium scoring in non-contrast-enhanced ECGtriggered cardiac CT with ambiguity detection,' IEEE Transactions on Medical Imaging , vol. 34, no. 9, pp. 1867-1878, Sep 2015. -
  17. [20][20] F. Durlak, M. Wels, C. Schwemmer, M. S¨ uhling, S. Steidl, and A. Maier, Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring , ser. Lecture Notes in Computer Science. Springer, Cham, Sep 2017, pp. 27-35. -
  18. [21][21] J. M. Wolterink, T. Leiner, M. A. Viergever, and I. Iˇ sgum, 'Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks,' in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015 , N. Navab, J. Hornegger, W. M. Wells, and A. Frangi, Eds. Cham: Springer International Publishing, 2015, pp. 589-596. -
  19. [23][23] N. Lessmann, I. Iˇ sgum, A. A. A. Setio, B. D. de Vos, F. Ciompi, P. A. de Jong, M. Oudkerk, W. P. T. M. Mali, M. A. Viergever, and B. van Ginneken, 'Deep convolutional neural networks for automatic coronary calcium scoring in a screening study with low-dose chest CT,' in Proceedings of SPIE , G. D. Tourassi and S. G. Armato, Eds., vol. 9785, Mar 2016, p. 978511. -
  20. [24][24] N. Lessmann, B. v. Ginneken, M. Zreik, P. A. de Jong, B. D. de Vos, M. A. Viergever, and I. Iˇ sgum, 'Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions,' IEEE Transactions on Medical Imaging , vol. 37, no. 2, pp. 615-625, Feb 2018. -
  21. [25][25] J. M. Wolterink, T. Leiner, B. D. de Vos, J.-L. Coatrieux, B. M. Kelm, S. Kondo, R. A. Salgado, R. Shahzad, H. Shu, M. Snoeren, R. A. P. Takx, L. J. van Vliet, T. van Walsum, T. P. Willems, G. Yang, Y. Zheng, M. A. Viergever, and I. Igum, 'An evaluation of automatic coronary artery calcium scoring methods with cardiac ct using the orcascore framework,' Medical Physics , vol. 43, no. 5, pp. 2361-2373, 2016. -
  22. [26][26] B. D. de Vos, J. M. Wolterink, P. A. de Jong, T. Leiner, M. A. Viergever, and I. Iˇ sgum, 'Convnet-based localization of anatomical structures in 3d medical images,' IEEE Transactions on Medical Imaging , vol. 36, no. 7, pp. 1470-1481, July 2017. -
  23. [27][27] M. A. Hussain, A. Amir-Khalili, G. Hamarneh, and R. Abugharbieh, 'Segmentation-free kidney localization and volume estimation using aggregated orthogonal decision cnns,' in Medical Image Computing and Computer-Assisted Intervention - MICCAI 2017 , M. Descoteaux, L. Maier-Hein, A. Franz, P. Jannin, D. L. Collins, and S. Duchesne, Eds. Cham: Springer International Publishing, 2017, pp. 612-620. -
  24. [28][28] X. Zhen, H. Zhang, A. Islam, M. Bhaduri, I. Chan, and S. Li, 'Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression,' Medical Image Analysis , vol. 36, pp. 184 - 196, 2017. -
  25. [29][29] W. Xue, G. Brahm, S. Pandey, S. Leung, and S. Li, 'Full left ventricle quantification via deep multitask relationships learning,' Medical Image Analysis , vol. 43, pp. 54 - 65, 2018. -
  26. [31][31] B. D. de Vos, N. Lessmann, P. A. de Jong, M. A. Viergever, and I. Iˇ sgum, 'Direct and real-time cardiovascular risk prediction,' arXiv:1712.02982 [cs] , 2017. - · arXiv
  27. [32][32] I. Iˇ sgum, B. D. de Vos, J. M. Wolterink, D. Dey, D. S. Berman, M. Rubeaux, T. Leiner, and P. J. Slomka, 'Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT,' Journal of Nuclear Cardiology , Apr 2017. -
  28. [33][33] B. D. de Vos, F. F. Berendsen, M. A. Viergever, M. Staring, and I. Iˇ sgum, 'End-to-end unsupervised deformable image registration with a convolutional neural network,' in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support: Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Qu´ ebec City, QC, Canada, September 14, Proceedings . Cham: Springer International Publishing, 2017, pp. 204-212. -
  29. [35][35] A. Rutten, I. Iˇ sgum, and M. Prokop, 'Calcium scoring with prospectively ECG-triggered CT: using overlapping datasets generated with MPR decreases inter-scan variability,' European Journal of Radiology , vol. 80, no. 1, pp. 83-88, Oct 2011. -
  30. [36][36] C. Jongen, J. P. W. Pluim, P. J. Nederkoorn, M. A. Viergever, and W. J. Niessen, 'Construction and evaluation of an average CT brain image for inter-subject registration,' Computers in Biology and Medicine , vol. 34, no. 8, pp. 647-662, Dec 2004. -
  31. [37][37] B. Ohnesorge, T. Flohr, R. Fischbach, A. Kopp, A. Knez, S. Schr¨ oder, U. Sch¨ opf, A. Crispin, E. Klotz, M. Reiser, and et al., 'Reproducibility of coronary calcium quantification in repeat examinations with retrospectively ECG-gated multisection spiral CT,' European Radiology , vol. 12, no. 6, pp. 1532-1540, Jun 2002. -
  32. [38][38] S. Ioffe and C. Szegedy, 'Batch normalization: Accelerating deep network training by reducing internal covariate shift,' in International Conference on Machine Learning , Jun 2015, pp. 448-456. -
  33. [39][39] D.-A. Clevert, T. Unterthiner, and S. Hochreiter, 'Fast and accurate deep network learning by exponential linear units (elus),' in International Conference on Machine Learning , 2016, arXiv: 1511.07289. - · arXiv
  34. [40][40] M. D. Zeiler and R. Fergus, 'Visualizing and understanding convolutional networks,' in Computer Vision -European Conference on Computer Vision 2014 , D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds., vol. 8689. Springer International Publishing, 2014, pp. 818-833. -
  35. [41][41] Theano Development Team, 'Theano: A Python framework for fast computation of mathematical expressions,' arXiv e-prints , vol. abs/1605.02688, 2016. - · arXiv
  36. [43][43] G. Bradski, 'The OpenCV Library,' Dr. Dobb's Journal of Software Tools , 2000. -
  37. [45][45] M. L. McHugh, 'Interrater reliability: the kappa statistic,' Biochem Med (Zagreb) , vol. 22, no. 3, pp. 276-282, 2012. -
  38. [46][46] C. Cano-Espinosa, G. Gonz´ alez, G. R. Washko, M. Cazorla, and R. S. J. Est´ epar, 'Automated agatston score computation in non-ECG gated CT scans using deep learning,' in Proceedings of SPIE , vol. 10574, 2018, pp. 10 574 - 10 574 - 6. -
  39. [47][47] E. A. Regan, J. E. Hokanson, J. R. Murphy, B. Make, D. A. Lynch, T. H. Beaty, D. Curran-Everett, E. K. Silverman, and J. D. Crapo, 'Genetic epidemiology of COPD (COPDGene) study design,' COPD , vol. 7, no. 1, pp. 32-43, Feb 2010. -

Cited by

Sorted by citations

Authors
de Vos, B. D., Wolterink, J. M., Leiner, T., de Jong, P. A., Lessmann, N., & Isgum, I.
Affiliations
Utrecht University (NL)
ORCID
Venue
IEEE Transactions on Medical Imaging
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2019-09
Volume
38
Issue
9
Pages
2127-2138
ISSN
  • 0278-0062 (ISSN-L)
  • 1558-254X
DOI
10.1109/tmi.2019.2899534
OpenAlex
W2915053383
Cited by
134
References
6
Citations by year
2026: 9 · 2025: 16 · 2024: 19 · 2023: 12 · 2022: 32 · 2021: 23 · 2020: 15 · 2019: 8
FWCI
12.89
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Medical Imaging (publishedVersion)
  • PubMed (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Stichting voor de Technische Wetenschappen (12726)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Cardiac Imaging and Diagnostics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
coronary artery calcium scoring, convolutional neural networks, CT registration, chest CT, cardiac CT, cardiovascular risk stratification, decision feedback
MeSH terms
Deep Learning, Coronary Artery Disease, Coronary Vessels, Diagnostic Techniques, Cardiovascular, Humans, Image Interpretation, Computer-Assisted, Radiography, Thoracic, Tomography, X-Ray Computed, Vascular Calcification

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