Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography

Journal of Medical Imaging

Volumn 2, Issue 1, 2015, Pages

  Kang, Dongwoo ^a   Dey, Damini ^b   Slomka, Piotr J ^b   Arsanjani, Reza ^b   Nakazato, Ryo ^b   Ko, Hyunsuk ^a   Berman, Daniel S ^b   Li, Debiao ^b   Kuo, C C Jay ^a  

^a University of Southern California   (United States)

^b CEDARS SINAI MEDICAL CENTER   (United States)

Author keywords

coronary arterial disease; coronary arterial lesion detection from coronary computed tomography angiography; coronary computed tomography angiography; image feature extraction; learning based detection; machine learning; structured learning; support vector machines; support vector regression

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; CLINICAL ARTICLE; COMPUTED TOMOGRAPHIC ANGIOGRAPHY; COMPUTED TOMOGRAPHY SCANNER; CONSENSUS; CORONARY ARTERY OBSTRUCTION; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; HUMAN; LEARNING ALGORITHM; MACHINE LEARNING; QUANTITATIVE ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE;

EID: 85015209012     PISSN: 23294302     EISSN: 23294310     Source Type: Journal    
DOI: 10.1117/1.JMI.2.1.014003     Document Type: Article

References (55)

1. S. Yusuf et al., "Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization," Circulation 104(22), 2746-2753 (2001).
2. S. Achenbach et al., "Randomized comparison of 64-slice singleand dual-source computed tomography coronary angiography for the detection of coronary artery disease," JACC Cardiovasc. Imaging 1(2), 177-186 (2008).
3. M. J. Budoff et al., "Diagnostic performance of 64-multidetector row coronary computed tomographic angiography for evaluation of coronary artery stenosis in individuals without known coronary artery disease: results from the prospective multicenter ACCURACY (Assessment by Coronary Computed Tomographic Angiography of Individuals Undergoing Invasive Coronary Angiography) trial," J. Am. Coll. Cardiol. 52(21), 1724-1732 (2008).
4. J. Hausleiter et al., "Non-invasive coronary computed tomographic angiography for patients with suspected coronary artery disease: the Coronary Angiography by Computed Tomography with the Use of a Submillimeter resolution (CACTUS) trial," Eur. Heart J. 28(24), 3034-3041 (2007).
5. W. B. Meijboom et al., "64-slice computed tomography coronary angiography in patients with high, intermediate, or low pretest probability of significant coronary artery disease," J. Am. Coll. Cardiol. 50(15), 1469-1475 (2007).
6. J. M. Miller et al., "Diagnostic performance of coronary angiography by 64-row CT," N. Engl. J. Med. 359(22), 2324-2336 (2008).
7. S. Achenbach et al., "Detection of calcified and noncalcified coronary atherosclerotic plaque by contrast-enhanced, submillimeter multidetector spiral computed tomography: a segment-based comparison with intravascular ultrasound," Circulation 109(1), 14-17 (2004).
8. A.W. Leber et al., "Accuracy of 64-slice computed tomography to classify and quantify plaque volumes in the proximal coronary system: a comparative study using intravascular ultrasound," J. Am. Coll. Cardiol. 47(3), 672-677 (2006).
9. M. Petranovic et al., "Assessment of nonstenotic coronary lesions by 64-slice multidetector computed tomography in comparison to intravascular ultrasound: evaluation of nonculprit coronary lesions," J. Cardiovasc. Comput. Tomogr. 3(1), 24-31 (2009).
10. F. Pugliese et al., "Learning curve for coronary CT angiography: what constitutes sufficient training?," Radiology 251(2), 359-368 (2009).
11. Y. Zheng et al., "Model-driven centerline extraction for severely occluded major coronary arteries," in Proc. Int. Workshop on Machine Learning in Medical Imaging, pp. 10-18, Springer Berlin Heidelberg (2012).
12. I. C. Sluimer et al., "Computer-aided diagnosis in high resolution CT of the lungs," Med. Phys. 30(12), 3081-3090 (2003).
13. L. Sorensen, S. B. Shaker, and M. de Bruijne, "Quantitative analysis of pulmonary emphysema using local binary patterns," IEEE Trans. Med. Imaging 29(2), 559-569 (2010).
14. T. Stavngaard et al., "Quantitative assessment of regional emphysema distribution in patients with chronic obstructive pulmonary disease (COPD)," Acta Radiol. 47(9), 914-921 (2006).
15. R. Uppaluri et al., "Computer recognition of regional lung disease patterns," Am. J. Respir. Crit. Care Med. 160(2), 648-654 (1999).
16. D. Wormanns et al., "Automatic detection of pulmonary nodules at spiral CT: clinical application of a computer-aided diagnosis system," Eur. Radiol. 12(5), 1052-1057 (2002).
17. S. B. Gokturk et al., "A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography," IEEE Trans. Med. Imaging 20(12), 1251-1260 (2001).
18. G. Kiss et al., "Computer-aided diagnosis in virtual colonography via combination of surface normal and sphere fitting methods," Eur. Radiol. 12(1), 77-81 (2002).
19. R. J. T. Sadleir and P. F. Whelan, "Colon centreline calculation for CT colonography using optimised 3D topological thinning," in First Int. Symp. on 3D Data Processing Visualization and Transmission, pp. 800-803, IEEE Computer Society, Los Alamitos, California (2002).
20. P. Sundaram et al., "Colon polyp detection using smoothed shape operators: preliminary results," Med. Image Anal. 12(2), 99-119 (2008).
21. T. W. Freer and M. J. Ulissey, "Screening mammography with computer-aided detection: prospective study of 12,860 patients in a community breast center," Radiology 220(3), 781-786 (2001).
22. S. Nawano et al., "Computer-aided diagnosis in full digital mammography," Invest. Radiol. 34(4), 310-316 (1999).
23. R. M. Nishikawa, "Current status and future directions of computeraided diagnosis in mammography," Comput. Med. Imaging Graph. 31(4-5), 224-235 (2007).
24. T. Tanaka et al., "Evaluation of computer-aided detection of lesions in mammograms obtained with a digital phase-contrast mammography system," Eur. Radiol. 19(12), 2886-2895 (2009).
25. M. J. Boogers et al., "Automated quantification of coronary plaque with computed tomography: comparison with intravascular ultrasound using a dedicated registration algorithm for fusion-based quantification," Eur. Heart J. 33(8), 1007-1016 (2012).
26. M. A. de Graaf et al., "Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology," Int. J. Cardiovasc. Imaging 29(5), 1177-1190 (2013).
27. D. Dey et al., "Automated 3-dimensional quantification of noncalcified and calcified coronary plaque from coronary CT angiography," J. Cardiovasc. Comput. Tomogr. 3(6), 372-382 (2009).
28. D. Dey et al., "Automated three-dimensional quantification of noncalcified coronary plaque from coronary CTangiography: comparison with intravascular US," Radiology 257(2), 516-522 (2010).
29. E. Arnoldi et al., "Automated computer-aided stenosis detection at coronary CT angiography: initial experience," Eur. Radiol. 20(5), 1160-1167 (2010).
30. M. S. Dinesh, P. Devarakota, and J. Kumar, "Automatic detection of plaques with severe stenosis in coronary vessels of CT angiography," Proc. SPIE 7624, 76242Q (2010).
31. E. J. Halpern and D. J. Halpern, "Diagnosis of coronary stenosis with CT angiography comparison of automated computer diagnosis with expert readings," Acad. Radiol. 18(3), 324-333 (2011).
32. B. M. Kelm et al., "Detection, grading and classification of coronary stenoses in computed tomography angiography," Med. Image Comput. Comput. Assist. Interv. 6893, 25-32 (2011).
33. R. Goldenberg et al., "Computer-aided simple triage (CAST) for coronary CT angiography (CCTA)," Int. J. Comput. Assist. Radiol. Surg. 7(6), 819-827 (2012).
34. T. S. Kristensen et al., "Prognostic implications of nonobstructive coronary plaques in patients with non-ST-segment elevation myocardial infarction: a multidetector computed tomography study," J. Am. Coll. Cardiol. 58(5), 502-509 (2011).
35. G. W. Stone et al., "A prospective natural-history study of coronary atherosclerosis," N. Engl. J. Med. 364(3), 226-235 (2011).
36. D. Kang et al., "Automated knowledge-based detection of nonobstructive and obstructive arterial lesions from coronary CT angiography," Med. Phys. 40(4), 041912 (2013).
37. D. Kang et al., "Automatic detection of significant and subtle arterial lesions from coronary CT angiography," Proc. SPIE 8314, 831435 (2012).
38. H. A. Kirisli et al., "Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography," Med. Image Anal. 17(8), 859-876 (2013).
39. K. Suzuki et al., "Recognition of coronary arterial stenosis using neural network on DSA system," Syst. Comput. Japan 26(8), 66-74 (1995).
40. M. Duval et al., "Coronary artery stenoses detection with random forest," 2012, http://coronary.bigr.nl/stenoses/pdf/Duval-UCLan-UNS-230.pdf (13 February 2015).
41. S. Mittal et al., "Fast automatic detection of calcified coronary lesions in 3D cardiac CT images," Lec. Notes Comput. Sci. 6357, 1-9 (2010).
42. Y. V. Sun et al., "Application of machine learning algorithms to predict coronary artery calcification with a sibship-based design," Genet. Epidemiol. 32(4), 350-360 (2008).
43. T. K. Ho, J. J. Hull, and S. N. Srihari, "Decision combination in multiple classifier systems," IEEE Trans. Pattern Anal. Mach. Intell. 16(1), 66-75 (1994).
44. G. Brunner et al., "Toward the automatic detection of coronary artery calcification in non-contrast computed tomography data," Int. J. Cardiovasc. Imaging 26(7), 829-838 (2010).
45. I. Isgum et al., "Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease," Med. Phys. 34(4), 1450-1461 (2007).
46. C. Cortes and V. Vapnik, "Support-vector networks," Mach. Learn. 20(3), 273-297 (1995).
47. J. T. Dodge, Jr. et al., "Intrathoracic spatial location of specified coronary segments on the normal human heart. Applications in quantitative arteriography, assessment of regional risk and contraction, and anatomic display," Circulation 78(5), 1167-1180 (1988).
48. G. L. Raff et al., "SCCT guidelines for the interpretation and reporting of coronary computed tomographic angiography," J. Cardiovasc. Comput. Tomogr. 3(2), 122-136 (2009).
49. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection," in Proc. of the 14th Int. Joint Conf. on Artificial intelligence (IJCAI'95), Vol. 2, pp. 1137-1143, Morgan Kaufmann Publishers Inc., San Francisco, California (1995).
50. H. B. He and E. A. Garcia, "Learning from imbalanced data," IEEE Trans. Knowl. Data Eng. 21(9), 1263-1284 (2009).
51. H. Drucker et al., "Support vector regression machines," in Advances in Neural Information Processing Systems, pp. 155-161, MIT Press, Cambridge (1997).
52. E. R. DeLong, D. M. DeLong, and D. L. Clarke-Pearson, "Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach," Biometrics 44(3), 837-845 (1988).
53. N. V. Chawla, N. Japkowicz, and A. Kotcz, "Editorial: special issue on learning from imbalanced data sets," SIGKDD Explor. 6(1), 1-6 (2004).
54. X. Guo et al., "On the class imbalance problem," in Fourth Int. Conf. on Natural Computation (ICNC'08), Vol. 4, pp. 192, 201 (2008).
55. N. Japkowicz and S. Stephen, "The class imbalance problem: a systematic study," Intell. Data Anal. 6(5), 429-449 (2002).