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Automated segmentation and quantification of aortic calcification at abdominal CT: application of a deep learning-based algorithm to a longitudinal screening cohort

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Abstract

Objective

To investigate an automated aortic calcium segmentation and scoring tool at abdominal CT in an adult screening cohort.

Methods

Using instance segmentation with convolutional neural networks (Mask R-CNN), a fully automated vascular calcification algorithm was applied to a data set of 9914 non-contrast CT scans from 9032 consecutive asymptomatic adults (mean age, 57.5 ± 7.8 years; 4467 M/5447F) undergoing colonography screening. Follow-up scans were performed in a subset of 866 individuals (mean interval, 5.4 years). Automated abdominal aortic calcium volume, mass, and Agatston score were assessed. In addition, comparison was made with a separate validated semi-automated approach in a subset of 812 cases.

Results

Mean values were significantly higher in males for Agatston score (924.2 ± 2066.2 vs. 564.2 ± 1484.2, p < 0.001), aortic calcium mass (222.2 ± 526.0 mg vs. 144.5 ± 405.4 mg, p < 0.001) and volume (699.4 ± 1552.4 ml vs. 426.9 ± 1115.5 HU, p < 0.001). Overall age-specific Agatston scores increased an average of 10%/year for the entire cohort; males had a larger Agatston score increase between the ages of 40 to 60 than females (91.2% vs. 75.1%, p < 0.001) and had significantly higher mean Agatston scores between ages 50 and 80 (p < 0.001). For the 812-scan subset with both automated and semi-automated methods, median difference in Agatston score was 66.4 with an r2 agreement value of 0.84. Among the 866-patient cohort with longitudinal follow-up, the average Agatston score change was 524.1 ± 1317.5 (median 130.9), reflecting a mean increase of 25.5% (median 73.6%).

Conclusion

This robust, fully automated abdominal aortic calcification scoring tool allows for both individualized and population-based assessment. Such data could be automatically derived at non-contrast abdominal CT, regardless of the study indication, allowing for opportunistic assessment of cardiovascular risk.

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Abbreviations

R-CNN:

Convolutional neural networks

CVD:

Cardiovascular disease

CT:

Computed tomography

ROI:

Region of interest

HU:

Hounsfield unit

References

  1. Benjamin, E.J., et al., Heart Disease and Stroke Statistics-2017 Update: A Report From the American Heart Association. Circulation, 2017. 135(10): p. e146-e603.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Sidney, S., et al., Recent Trends in Cardiovascular Mortality in the United States and Public Health Goals. JAMA Cardiol, 2016. 1(5): p. 594-9

    Article  PubMed  Google Scholar 

  3. Hajar, R., Framingham Contribution to Cardiovascular Disease. Heart views: the official journal of the Gulf Heart Association, 2016. 17(2): p. 78-81.

    Article  Google Scholar 

  4. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). Jama, 2001. 285(19): p. 2486-97.

  5. James, P.A., et al., 2014 evidence-based guideline for the management of high blood pressure in adults: report from the panel members appointed to the Eighth Joint National Committee (JNC 8). Jama, 2014. 311(5): p. 507-20.

    Article  CAS  PubMed  Google Scholar 

  6. Alqahtani, A.M., et al., Quantifying Aortic Valve Calcification using Coronary Computed Tomography Angiography. J Cardiovasc Comput Tomogr, 2017. 11(2): p. 99-104.

    Article  PubMed  Google Scholar 

  7. Budoff, M.J., et al., Thoracic aortic calcification and coronary heart disease events: the multi-ethnic study of atherosclerosis (MESA). Atherosclerosis, 2011. 215(1): p. 196-202.

    Article  CAS  PubMed  Google Scholar 

  8. DeLoach, S.S., et al., Aortic calcification predicts cardiovascular events and all-cause mortality in renal transplantation. Nephrology, dialysis, transplantation: official publication of the European Dialysis and Transplant Association - European Renal Association, 2009. 24(4): p. 1314-1319.

    Article  Google Scholar 

  9. O’Leary, D.H., et al., Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults. Cardiovascular Health Study Collaborative Research Group. N Engl J Med, 1999. 340(1): p. 14-22.

  10. Pletcher, M.J., et al., Using the coronary artery calcium score to predict coronary heart disease events: a systematic review and meta-analysis. Arch Intern Med, 2004. 164(12): p. 1285-92.

    Article  PubMed  Google Scholar 

  11. Eberhard, M., et al., Quantification of aortic valve calcification on contrast-enhanced CT of patients prior to transcatheter aortic valve implantation. EuroIntervention, 2017. 13(8): p. 921-927.

    Article  PubMed  Google Scholar 

  12. Gernaat, S.A.M., et al., Automatic quantification of calcifications in the coronary arteries and thoracic aorta on radiotherapy planning CT scans of Western and Asian breast cancer patients. Radiother Oncol, 2018. 127(3): p. 487-492.

    Article  PubMed  Google Scholar 

  13. Isgum, I., B. van Ginneken, and M. Olree, Automatic detection of calcifications in the aorta from CT scans of the abdomen. 3D computer-aided diagnosis. Acad Radiol, 2004. 11(3): p. 247-57.

  14. Zoghbi, W.A., Cardiovascular imaging: a glimpse into the future. Methodist DeBakey cardiovascular journal, 2014. 10(3): p. 139-145.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Elmasri, K., et al., Automatic Detection and Quantification of Abdominal Aortic Calcification in Dual Energy X-ray Absorptiometry. Procedia Computer Science, 2016. 96: p. 1011-1021.

    Article  Google Scholar 

  16. Kurugol, S., et al., Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions. Medical physics, 2015. 42(9): p. 5467-5478.

    Article  PubMed  PubMed Central  Google Scholar 

  17. O’Connor, S.D., et al., Does Nonenhanced CT-based Quantification of Abdominal Aortic Calcification Outperform the Framingham Risk Score in Predicting Cardiovascular Events in Asymptomatic Adults? Radiology, 2019. 290(1): p. 108-115.

    Article  PubMed  Google Scholar 

  18. Pickhardt, P.J., Imaging and Screening for Colorectal Cancer with CT Colonography. Radiol Clin North Am, 2017. 55(6): p. 1183-1196.

    Article  PubMed  Google Scholar 

  19. Chellamuthu, K., et al., Atherosclerotic Vascular Calcification Detection and Segmentation on Low Dose Computed Tomography Scans Using Convolutional Neural Networks, in IEEE ISBI. 2017: Melbourne, Australia. p. 388-391.

  20. Liu, J., et al., Pelvic artery calcification detection on CT scans using convolutional neural networks, in SPIE Medical Imaging, S.G. Armato and N.A. Petrick, Editors. 2017. p. 101341A.

  21. Liu, J., et al., A Semi-Supervised CNN Learning Method with Pseudo-class Labels for Atherosclerotic Vascular Calcification Detection, in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), Venice, Italy, April 8-11, 2019. pp. 780-783.

  22. Yao, J., O’Connor, S.D. and Summers, R.M. Automated spinal column extraction and partitioning. in 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006. 2006.

  23. He, K.M., et al., Mask R-CNN. 2017 Ieee International Conference on Computer Vision (Iccv), 2017: p. 2980-2988.

    Article  Google Scholar 

  24. Rumberger, J.A. and L. Kaufman, A Rosetta Stone for Coronary Calcium Risk Stratification: Agatston, Volume, and Mass Scores in 11,490 Individuals. American Journal of Roentgenology, 2003. 181(3): p. 743-748.

    Article  PubMed  Google Scholar 

  25. Dudina, A., et al., Relationships between body mass index, cardiovascular mortality, and risk factors: a report from the SCORE investigators. Eur J Cardiovasc Prev Rehabil, 2011. 18(5): p. 731-42.

    Article  PubMed  Google Scholar 

  26. Khan, S.S., et al., Association of Body Mass Index With Lifetime Risk of Cardiovascular Disease and Compression of Morbidity. JAMA Cardiol, 2018. 3(4): p. 280-287.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Mancio, J., et al., Association of body mass index and visceral fat with aortic valve calcification and mortality after transcatheter aortic valve replacement: the obesity paradox in severe aortic stenosis. Diabetol Metab Syndr, 2017. 9: p. 86.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Glodny, B., et al., A method for calcium quantification by means of CT coronary angiography using 64-multidetector CT: very high correlation with Agatston and volume scores. Eur Radiol, 2009. 19(7): p. 1661-8.

    Article  PubMed  Google Scholar 

  29. Laudon, D.A., et al., Computed tomographic coronary artery calcium assessment for evaluating chest pain in the emergency department: long-term outcome of a prospective blind study. Mayo Clinic proceedings, 2010. 85(4): p. 314-322.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Li, Q., et al., Coronary artery calcium quantification using contrast-enhanced dual-energy computed tomography scans in comparison with unenhanced single-energy scans. Phys Med Biol, 2018. 63(17): p. 175006.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Moreno, C.C., et al., Changing Abdominal Imaging Utilization Patterns: Perspectives From Medicare Beneficiaries Over Two Decades. Journal of the American College of Radiology, 2016. 13(8): p. 894-903.

    Article  PubMed  Google Scholar 

  32. Lee, S.J. and P.J. Pickhardt, Opportunistic Screening for Osteoporosis Using Body CT Scans Obtained for Other Indications: the UW Experience. Clinical Reviews in Bone and Mineral Metabolism, 2017. 15(3): p. 128-137.

    Article  CAS  Google Scholar 

  33. Pickhardt, P.J., et al., Opportunistic Screening for Osteoporosis Using Abdominal Computed Tomography Scans Obtained for Other Indications. Annals of Internal Medicine, 2013. 158(8): p. 588-595.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Boyce, C.J., et al., Hepatic Steatosis (Fatty Liver Disease) in Asymptomatic Adults Identified by Unenhanced Low-Dose CT. American Journal of Roentgenology, 2010. 194(3): p. 623-628.

    Article  PubMed  Google Scholar 

  35. Pickhardt, P.J., et al., Natural History of Hepatic Steatosis: Observed Outcomes for Subsequent Liver and Cardiovascular Complications. American Journal of Roentgenology, 2014. 202(4): p. 752-758.

    Article  PubMed  Google Scholar 

  36. Pickhardt, P.J., et al., Visceral Adiposity and Hepatic Steatosis at Abdominal CT: Association With the Metabolic Syndrome. American Journal of Roentgenology, 2012. 198(5): p. 1100-1107.

    Article  PubMed  Google Scholar 

  37. Pickhardt, P.J., et al., CT colonography to screen for colorectal cancer and aortic aneurysm in the Medicare population: cost-effectiveness analysis. AJR Am J Roentgenol, 2009. 192(5): p. 1332-40.

    Article  PubMed  Google Scholar 

  38. Lee, S.J., et al., Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort. Br J Radiol, 2018. 91(1089): p. 20170968.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Lee, S.J., P.A. Anderson, and P.J. Pickhardt, Predicting Future Hip Fractures on Routine Abdominal CT Using Opportunistic Osteoporosis Screening Measures: A Matched Case-Control Study. AJR Am J Roentgenol, 2017. 209(2): p. 395-402.

    Article  PubMed  Google Scholar 

  40. Lee, S.J., et al., Future Osteoporotic Fracture Risk Related to Lumbar Vertebral Trabecular Attenuation Measured at Routine Body CT. J Bone Miner Res, 2018. 33(5): p. 860-867.

    Article  CAS  PubMed  Google Scholar 

  41. Pickhardt, P.J., et al., Population-based opportunistic osteoporosis screening: Validation of a fully automated CT tool for assessing longitudinal BMD changes. British Journal of Radiology, 2019. 92(1094).

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Acknowledgement

This research was supported in part by the Intramural Research Program of the National Institutes of Health Clinical Center and made use of the high performance computing capabilities of the NIH Biowulf system.

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Authors and Affiliations

  1. E3/311 Clinical Science Center, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave., Madison, WI, 53792-3252, USA

    Peter M. Graffy & Perry J. Pickhardt

  2. Radiology & Imaging Sciences, National Institutes of Health Clinical Center, Bethesda, MD, USA

    Jiamin Liu & Ronald M. Summers

  3. Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, USA

    Stacy O’Connor

Authors
  1. Peter M. Graffy
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  2. Jiamin Liu
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  3. Stacy O’Connor
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  4. Ronald M. Summers
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  5. Perry J. Pickhardt
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Corresponding author

Correspondence to Perry J. Pickhardt.

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Conflict of interest

The authors have no direct conflict of interest, but Dr. Pickhardt serves as an advisor to Bracco and is a shareholder in SHINE, Elucent, and Cellectar and Dr. Summers receives royalties from iCAD, PingAn, Philips and ScanMed and research support from PingAn and NVIDIA.

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This study was approved by our institutional IRB.

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Graffy, P.M., Liu, J., O’Connor, S. et al. Automated segmentation and quantification of aortic calcification at abdominal CT: application of a deep learning-based algorithm to a longitudinal screening cohort. Abdom Radiol 44, 2921–2928 (2019). https://doi.org/10.1007/s00261-019-02014-2

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  • DOI: https://doi.org/10.1007/s00261-019-02014-2

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