Review. Automatic Segmentation Techniques of the Coronary Artery Using CT Images in Acute Coronary Syndromes

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Abstract

Coronary artery disease represents one of the leading reasons of death worldwide, and acute coronary syndromes are their most devastating consequences. It is extremely important to identify the patients at risk for developing an acute myocardial infarction, and this goal can be achieved using noninvasive imaging techniques. Coronary computed tomography angiography (CCTA) is currently one of the most reliable methods used for assessing the coronary arteries; however, its use in emergency settings is sometimes limited due to time constraints. This paper presents the main characteristics of plaque vulnerability, the role of CCTA in the assessment of vulnerable plaques, and automatic segmentation techniques of the coronary artery tree based on CT angiography images. A detailed inventory of existing methods is given, representing the state-of-the-art of computational methods applied in vascular system segmentation, focusing on the current applications in acute coronary syndromes.

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  • 1. Roger VL Go AS Lloyd-Jones DM et al. Heart disease and stroke statistics - 2012 update: a report from the American Heart Association. Circulation. 2012;125:e2-e220. doi:

    • Crossref
    • Export Citation
  • 2. World Health Organization Cardiovascular disease (CVDs) 2016. Available from: http://www.who.int/mediacentre/factsheets/fs317/en/#

  • 3. Choy SY Mintz GS. What have we learned about plaque rupture in acute coronary syndromes? Curr Cardiol Rep. 2010;12:338-343. doi:

    • Crossref
    • Export Citation
  • 4. Tominaga J Fukunaga Y Abelardo E Nagafuchi A. Defining the function of beta-catenin tyrosine phosphorylation in cadherin-mediated cell-cell adhesion. Genes Cells. 2008;13:67-77. doi:

    • Crossref
    • Export Citation
  • 5. Cheruvu PK Finn AV Gardner C et al. Frequency and distribution of thin-cap fibroatheroma and ruptured plaques in human coronary arteries: a pathologic study. J Am Coll Cardiol. 2007;50:940-949. doi:

    • Crossref
    • Export Citation
  • 6. Saybolt MD Lilly SM Patel D et al. The vulnerable artery: early and rapid deposition of lipid in coronary arteries is associated with subsequent development of thin-cap fibroatheromas. EuroIntervention. 2016;11:e1612-e1618. doi:

    • Crossref
    • Export Citation
  • 7. Stary HC Chandler AB Glagov S et al. A definition of initial fatty streak and intermediate lesions of atherosclerosis. A report from the Committee on Vascular Lesions of the Council on Arteriosclerosis American Heart Association. Circulation 1994;89:2462-2478.

  • 8. Finn AV Nakano M Narula J Kolodgie FD Virmani R. Concept of vulnerable/unstable plaque. Arterioscler Thromb Vasc Biol. 2010;30:1282-1292. doi:

    • Crossref
    • Export Citation
  • 9. Bourantas CV Garcia-Garcia HM Farooq V et al. Clinical and angiographic characteristics of patients likely to have vulnerable plaques: analysis from the PROSPECT study. JACC Cardiovasc Imaging. 2013;6:1263-1272. doi:

    • Crossref
    • Export Citation
  • 10. Bentzon JF Otsuka F Virmani R Falk E. Mechanisms of plaque formation and rupture. Circ Res. 2014;114:1852-1866. doi:

    • Crossref
    • Export Citation
  • 11. Schaar JA Muller JE Falk E et al. Terminology for highrisk and vulnerable coronary artery plaques. Eur Heart J. 2004;25:1077-1082. doi:

    • Crossref
    • Export Citation
  • 12. Varnava AM Mills PG Davies MJ. Relationship between coronary artery remodeling and plaque vulnerability. Circulation. 2002;105:939-943.

  • 13. Ota H Magalhaes MA Torguson R et al. The influence of lipid-containing plaque composition assessed by nearinfrared spectroscopy on coronary lesion remodelling. Eur Heart J Cardiovasc Imaging. 2016;17:821-831. doi:

    • Crossref
    • Export Citation
  • 14. Hansson GK Hermansson A. The immune system in atherosclerosis. Nat Immunol. 2011; 12:204-212. doi:

    • Crossref
    • Export Citation
  • 15. Newby A. Metalloproteinases and Vulnerable Atherosclerotic Plaques. Trends Cardiovasc Med. 2010;17:253-258. doi:

    • Crossref
    • Export Citation
  • 16. Taruya A Tanaka A Nishiguchi T et al. Vasa vasorum restructuring in human atherosclerotic plaque vulnerability: a clinical optical coherence tomography study. J Am Coll Cardiol. 2015;65:2469-2477. doi:

    • Crossref
    • Export Citation
  • 17. Yokoya K Takatsu H Suzuki T et al. Process of progression of coronary artery lesions from mild or moderate stenosis to moderate or severe stenosis: a study based on four serial coronary arteriograms per year. Circulation. 1999;100:903-909.

  • 18. Sakaguchi M Hasegawa T Ehara S et al. New insights into spotty calcification and plaque rupture in acute coronary syndrome: an optical coherence tomography study. Heart Vessels. 2016;31:1915-1922. Doi: 10.1007/s00380-016-0820-3.

  • 19. Jia H Dai J Hou J et al. Effective anti-thrombotic therapy without stenting: intravascular optical coherence tomography-based management in plaque erosion (the EROSION study). Eur Heart J. 2016;38:792-800. doi:

    • Crossref
    • Export Citation
  • 20. Bentzon JF Otsuka F Virmani R Falk E. Mechanisms of plaque formation and rupture. Circ Res. 2014;114:1852-1866. doi:

    • Crossref
    • Export Citation
  • 21. Stefanidis C Antoniou C Tsiachris D Pietri P. Coronary Atherosclerotic Vulnerable Plaque: Current Perspectives. J Am Heart Assoc. 2017;6:e005543. doi:

    • Crossref
    • Export Citation
  • 22. Thondapu V Bourantas C Foin N et al. Biomechanical stress in coronary atherosclerosis: emerging insights from computational modelling. Eur Heart J. 2017;38:81-92. doi:

    • Crossref
    • Export Citation
  • 23. McDaniel M Galbraith E Jeroudi A et al. Localization of culprit lesions in coronary arteries of patients with STsegment elevation myocardial infarctions: relation to bifurcations and curvatures. Am Heart J. 2011;161:508-515. doi:

    • Crossref
    • Export Citation
  • 24. Chiu J Chien S. Effects of Disturbed Flow on Vascular Endothelium: Pathophysiological Basis and Clinical Perspectives. Physiol Rev. 2011;91:327-387. doi:

    • Crossref
    • Export Citation
  • 25. Koskinas K Chatzizisis Y Baker A Edelman E Stone P Feldman L. The role of low endothelial shear stress in the conversion of atherosclerotic lesions from stable to unstable plaque. Curr Opin Cardiol. 2009;24:580-590. doi:

    • Crossref
    • Export Citation
  • 26. Cunningham KS Gotlieb AI. The role of shear stress in the pathogenesis of atherosclerosis. Lab Invest. 2005;85:9-23. doi:

    • Crossref
    • Export Citation
  • 27. Chen Z Peng C Cui X LiY S Chien S Shyy J. Shear stress SIRT 1 and vascular homeostasis. Proc Natl Acad Sci USA. 2010;107:10268-10273. doi:

    • Crossref
    • Export Citation
  • 28. Gambillara V Montorzi G Haziza-Pigeon C Stergiopulos N Silacci P. Arterial wall response to ex vivo exposure to oscillatory shear stress. J Vasc Res. 2005;42:535-544. doi:

    • Crossref
    • Export Citation
  • 29. Sucosky P Balachandran K Elhammali A Jo H Yoganathan AP. Altered shear stress stimulates upregulation of endothelial VCAM-1 and ICAM-1 in a BMP-4and TGF-beta1-dependent pathway. Arterioscler Thromb Vasc Biol. 2009;29:254-260. doi:

    • Crossref
    • Export Citation
  • 30. Gimbrone MA Topper JN Nagel T Anderson KR Garcia- Cardena G. Endothelial dysfunction hemodynamic forces and atherogenesis. Ann NY Acad Sci. 2000;902:230-240.

  • 31. Samady H Eshtehardi P McDaniel MC et al. Coronary artery wall shear stress is associated with progression and transformation of atherosclerotic plaque and arterial remodeling in patients with coronary artery disease. Circulation. 2011;124:779-788. doi:

    • Crossref
    • Export Citation
  • 32. Dancu MB Berardi DE Vanden Heuvel JP Tarbell JM. Asynchronous shear stress and circumferential strain reduces endothelial NO synthase and cyclooxygenase-2 but induces endothelin-1 gene expression in endothelial cells. Arterioscler Thromb Vasc Biol. 2004;24:2088-2094. doi:

    • Crossref
    • Export Citation
  • 33. Gijsen FJH Mastik F Schaar JA et al. High shear stress induces a strain increase in human coronary plaques over a 6-month period. EuroIntervention. 2011;7:121-127. doi:

    • Crossref
    • Export Citation
  • 34. Wang Y Qiu J Luo S et al. High shear stress induces atherosclerotic vulnerable plaque formation through angiogenesis. Regen Biomater. 2016;3:257-267. doi:

    • Crossref
    • Export Citation
  • 35. Kramer M Rittersma S de Winter R et al. Relationship of thrombus healing to underlying plaque morphology in sudden coronary death. J Am Coll Cardiol. 2010;55:122-132. doi:

    • Crossref
    • Export Citation
  • 36. Slager CJ Wentzel JJ Gijsen FJ et al. The role of shear stress in the generation of rupture-prone vulnerable plaques. Nat Clin Pract Cardiovasc Med. 2005;2:401-407.

  • 37. Wentzel JJ Chatzizisis YS Gijsen FJ et al. Endothelial shear stress in the evolution of coronary atherosclerotic plaque and vascular remodelling: current understanding and remaining questions. Cardiovasc Res. 2012;96:234-243. doi:

    • Crossref
    • Export Citation
  • 38. Chung WB Hamburg NM Holbrook M et al. The brachial artery remodels to maintain local shear stress despite the presence of cardiovascular risk factors. Arterioscler Thromb Vasc Biol. 2009;29:606-612. doi:

    • Crossref
    • Export Citation
  • 39. Freidja ML Toutain B Caillon A et al. Heme oxygenase 1 is differentially involved in blood flow-dependent arterial remodeling: role of inflammation oxidative stress and nitric oxide. Hypertension. 2011;58:225-231. doi:

    • Crossref
    • Export Citation
  • 40. Benedek T Mester A Benedek A Rat N Opincariu D Chițu M. Assessment of Coronary Plaque Vulnerability in Acute Coronary Syndromes using Optical Coherence Tomography or Intravascular Ultrasound. A systematic Review. Journal of Cardiovascular Emergencies. 2016;2:173-184. doi:

    • Crossref
    • Export Citation
  • 41. Sinclair H Veerasamy M Bourantas C et al. The role of virtual histology intravascular ultrasound in the identification of coronary artery plaque vulnerability in acute coronary syndromes. Cardiol Rev. 2016;24:303-309. doi:

    • Crossref
    • Export Citation
  • 42. Calvert PA Obaid DR O’Sullivan M et al. Association between IVUS findings and adverse outcomes in patients with coronary artery disease: the VIVA (VH-IVUS in Vulnerable Atherosclerosis) Study. JACC Cardiovasc Imaging. 2011;4:894-901. doi:

    • Crossref
    • Export Citation
  • 43. Sathyanarayana S Carlier S Li W Thomas L. Characterisation of atherosclerotic plaque by spectral similarity of radiofrequency intravascular ultrasound signals. EuroIntervention. 2009;5:133-139.

  • 44. Jang IK. Optical Coherence Tomography or Intravascular Ultrasound? JACC: Cardiovascular Interventions. 2011;4:492494. doi:

    • Crossref
    • Export Citation
  • 45. Finn AV Chandrashekhar Y Narula J. Vulnerable plaques: from PROSPECT to prospects... JACC Cardiovasc Imaging. 2012;5:334-336. doi:

    • Crossref
    • Export Citation
  • 46. Toutouzas K Synetos A Stefanadi E et al. Correlation between morphologic characteristics and local temperature differences in culprit lesions of patients with symptomatic coronary artery disease. J Am Coll Cardiol. 2007;49:2264-2271. doi:

    • Crossref
    • Export Citation
  • 47. Madder RD Goldstein JA Madden SP et al. Detection by nearinfrared spectroscopy of large lipid core plaques at culprit sites in patients with acute ST-segment elevation myocardial infarction. JACC Cardiovasc Interv. 2013;6:838-846. doi:

    • Crossref
    • Export Citation
  • 48. Ughi GJ Wang H Gerbaud E et al. Clinical characterization of coronary atherosclerosis with dual-modality OCT and nearinfrared autofluorescence imaging. JACC Cardiovasc Imaging. 2016;9:1304-1314.

  • 49. Matter C Stuber M Nahrendorf M. Imaging of the unstable plaque: how far have we got? Eur Heart J. 2009;30:2566-2574. doi:

    • Crossref
    • Export Citation
  • 50. Rodriguez-Granillo GA Carrascosa P Bruining N Waksman R Garcia-Garcia HM. Defining the non-vulnerable and vulnerable patients with computed tomography coronary angiography: evaluation of atherosclerotic plaque burden and composition. Eur Heart J Cardiovasc Imaging. 2016;17:481-491. doi:

    • Crossref
    • Export Citation
  • 51. Achenbach S Boehmer K Pflederer T et al. Influence of slice thickness and reconstruction kernel on the computed tomographic attenuation of coronary atherosclerotic plaque. J Cardiovasc Comput Tomogr. 2010;4:110-115. doi:

    • Crossref
    • Export Citation
  • 52. Yu L Leng S McCollough CH. Dual-energy CT-based monochromatic imaging. AJR Am J Roentgenol. 2012;199:S9- S15. doi:

    • Crossref
    • Export Citation
  • 53. Voros S Rinehart S Qian Z et al. Coronary atherosclerosis imaging by coronary CT angiography: current status correlation with intravascular interrogation and metaanalysis. JACC Cardiovasc Imaging. 2011;4:537-548. doi:

    • Crossref
    • Export Citation
  • 54. Motoyama S Ito H Sarai M et al. Plaque characterization by coronary computed tomography angiography and the likelihood of acute coronary events in mid-term followup. J Am Coll Cardiol. 2015;66:337-346. doi:

    • Crossref
    • Export Citation
  • 55. Li ZX Zhang YT Liu GZ Shao HY Li WM Tang XL. A robust coronary artery identification and centerline extraction method in angiographies. Biomed Sign Proc Contr. 2015;16:1-8. http://dx.doi.org/10.1016/j.bspc.2014.09.015.

  • 56. Benedek T Gyöngyösi M Benedek I. Multislice computed tomographic coronary angiography for quantitative assessment of culprit lesions in acute coronary syndromes. Can J Cardiol. 2013;29:364-371. doi:

    • Crossref
    • Export Citation
  • 57. Choi BJ Kang DK Tahk SJ et al. Comparison of 64-slice multidetector computed tomography with spectral analysis of intravascular ultrasound backscatter signals for characterizations of noncalcified coronary arterial plaques. Am J Cardiol. 2008;102:988-993. doi:

    • Crossref
    • Export Citation
  • 58. Motoyama S Masayoshi S Harigaya H et al. Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome. J Am Coll Cardiol. 2009;54:49-57. doi:

    • Crossref
    • Export Citation
  • 59. Kroener E van Velzen J Boogers M et al. Positive remodeling on coronary computed tomography as a marker for plaque vulnerability on virtual histology intravascular ultrasound. Am J Cardiol. 2011;107:1725-1729. doi:

    • Crossref
    • Export Citation
  • 60. Maurovich-Horvat P Schlett CL Alkadhi H et al. The napkin-ring sign indicates advanced atherosclerotic lesions in coronary CT angiography. JACC Cardiovasc Imaging. 2012;5:1243-1252. doi:

    • Crossref
    • Export Citation
  • 61. Collin J Gossl M Matsuo Y et al. Osteogenic monocytes within the coronary circulation and their association with plaque vulnerability in patients with early atherosclerosis. Int J Cardiol. 2015;181:57-64. doi:

    • Crossref
    • Export Citation
  • 62. Thomsen C Abdulla J. Characteristics of high-risk coronary plaques identified by computed tomographic angiography and associated prognosis: a systematic review and metaanalysis. Eur Heart J Cardiovasc Imaging. 2016;17:120-129. doi:

    • Crossref
    • Export Citation
  • 63. Hou ZH Lu B Gao Y et al. Prognostic value of coronary CT angiography and calcium score for major adverse cardiac events in outpatients. JACC Cardiovasc Imaging. 2012;5:990-999. doi:

    • Crossref
    • Export Citation
  • 64. Mester A Chitu M Rat N et al. CT Determination of Fractional Flow Reserve in Coronary Lesions. Journal of Interdisciplinary Medicine. 2016;1:237-241. doi:

    • Crossref
    • Export Citation
  • 65. Norgaard B Leipsic J Gaur S et al. Diagnostic performance of noninvasive fractional flow reserve derived from coronary computed tomography angiography in suspected coronary artery disease: the NXT trial (analysis of coronary blood flow using CT angiography: next steps). J Am Coll Cardiol. 2014;63:1145-1155. doi:

    • Crossref
    • Export Citation
  • 66. Orzan M Stanescu A Nyulas T et al. Transluminal Attenuation Gradient for the Noninvasive Assessment of Functional Significance in Coronary Artery Stenoses. Journal of Interdisciplinary Medicine. 2016;1:267-270. doi:

    • Crossref
    • Export Citation
  • 67. Brown AJ Teng Z Evans PC Gillard JH Samady H Bennett MR. Role of biomechanical forces in the natural history of coronary atherosclerosis. Nat Rev Cardiol. 2016;13:210-220. doi:

    • Crossref
    • Export Citation
  • 68. Ohayon J Finet G Le Floc’h S et al. Biomechanics of atherosclerotic coronary plaque: site stability and in vivo elasticity modeling. Ann Biomed Eng. 2014;42:269-279. doi:

    • Crossref
    • Export Citation
  • 69. Walsh MT Cunnane EM Mulvihill JJ Akyildiz AC Gijsen FJ Holzapfel GA. Uniaxial tensile testing approaches for characterisation of atherosclerotic plaques. J Biomech. 2014;47:793-804. doi:

    • Crossref
    • Export Citation
  • 70. Huang X Yang C Zheng J et al. 3D MRI-based multicomponent thin layer structure only plaque models for atherosclerotic plaques. J Biomech. 2016;49:2726-2733. doi:

    • Crossref
    • Export Citation
  • 71. Zhou A Chan H Chughtai A et al. Automated coronary artery tree extraction in coronary CT angiography using a multiscale enhancement and dynamic balloon tracking (MSCAR-DBT) method. Comput Med Imaging Graph. 2012;36:1-10. doi:

    • Crossref
    • Export Citation
  • 72. Han D Shim H Jeon B et al. Automatic Coronary Artery Segmentation Using Active Search for Branches and Seemingly Disconnected Vessel Segments from Coronary CT Angiography. PLoS One. 2016;11:e0156837. doi:

    • Crossref
    • Export Citation
  • 73. Truc PTH Khan AU Lee YK Lee SY Kim TS. Vessel enhancement filter using directional filter bank. Comput Vis Image Understand. 2009;113:101-112. http://dx.doi.org/10.1016/j.cviu.2008.07.009.

  • 74. Cetin S Demir A Yezzi A Degertekin M Unal G. Vessel tractography using an intensity based tensor model with branch detection. IEEE Trans Med Imag. 2013;32:348-363. doi:

    • Crossref
    • Export Citation
  • 75. Cetin S Unal G. A higher-order tensor vessel tractography for segmentation of vascular structures. IEEE Trans Med Imag. 2015;34:2172-2185. doi:

    • Crossref
    • Export Citation
  • 76. Becker C Rigamonti R Lepetit V Fua P. Supervised feature learning for curvilinear structure segmentation. Med Image Comput Comput Assist Interv. 2013;16:526-533.

  • 77. Su R Sun CM Pham TD. Junction detection for linear structures based on Hessian correlation and shape information. Patt Recogn. 2012;45:3695-3706. http://dx.doi.org/10.1016/j.patcog.2012.04.013.

  • 78. Wang S Wu JH Wei MQ Ma X. Robust curve skeleton extraction for vascular structures. Graph Models. 2012;74:109-120. http://dx.doi.org/10.1016/j.gmod.2012.03.008.

  • 79. Wong WCK So RWK Chung ACS. Principal curves for lumen center extraction and flow channel width estimation in 3-D arterial networks: Theory Algorithm and Validation. IEEE Trans Image Proc. 2012;21:1847-1862. doi:

    • Crossref
    • Export Citation
  • 80. Delibasis KK Kechriniotis AI Tsonos C Assimakis N. Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Comput Meth Prog Biomed. 2010;100:108-122. doi:

    • Crossref
    • Export Citation
  • 81. Wang Y Liatsis P. Automatic segmentation of coronary arteries in CT imaging in the presence of kissing vessel artifacts. IEEE Trans Inform Technol Biomed. 2012;16:782-789. doi:

    • Crossref
    • Export Citation
  • 82. Bauer C Pock T Sorantin E Bischof H Beichel R. Segmentation of interwoven 3D tubular tree structures utilizing shape priors and graph cuts. Med Image Anal. 2010;14:172-184. doi:

    • Crossref
    • Export Citation
  • 83. Zhou C Chan HP Chughtai A et al. Automated coronary artery tree extraction in coronary CT angiography using a multiscale enhancement and dynamic balloon tracking (MSCAR-DBT) method. Comput Med Imag Graph. 2012;36:1-10. doi:

    • Crossref
    • Export Citation
  • 84. Gülsün MA Funka-Lea G Zheng YF Eckert M. CTA coronary labeling through efficient geodesics between trees using anatomy priors. Med Image Comput Comput Assist Interv. 2014;17:521-528.

  • 85. Fabijanska A. Segmentation of pulmonary vascular tree from 3D CT thorax scans. Biocybern Biomed Eng. 2015;35:106-119. http://dx.doi.org/10.1016/j.bbe.2014.07.001.

  • 86. Orkisz M Hernandez Hoyos M Pérez Romanello V et al. Segmentation of the pulmonary vascular trees in 3D CT images using variational region-growing. IRBM. 2014;35:11-19. http://dx.doi.org/10.1016/j.irbm.2013.12.001.

  • 87. Rudyanto RD Kerkstra S van Rikxoort et al. Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study. Med Image Anal. 2014;18:1217-1232. doi:

    • Crossref
    • Export Citation
  • 88. Xiao CY Staring M Shamonin D Reiber JHC Stolk J Stoel BC. A strain energy filter for 3D vessel enhancement with application to pulmonary CT images. Med Image Anal. 2011;15:112-124. doi:

    • Crossref
    • Export Citation
  • 89. Forkert ND Schmidt-Richberg A Fiehler J et al. 3D cerebrovascular segmentation combining fuzzy vessel enhancement and level-sets with anisotropic energy weights. Magn Reson Imaging. 2013;31:262-71. doi:

    • Crossref
    • Export Citation
  • 90. Hassan M Chaudhry A Khan A Kim JY. Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering. Comput Methods Programs Biomed. 2012;108:1261-1276. doi:

    • Crossref
    • Export Citation
  • 91. Fathi A Naghsh-Nilchi AR. Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomed Sign Proc Contr. 2013;8:71-80. http://dx.doi.org/10.1016/j.bspc.2012.05.005.

  • 92. Moghimirad E Rezatofighi SH Soltanian-Zadeh. Retinal vessel segmentation using a multi-scale medialness function. Comput Biol Med. 2012;42:50-60. http://dx.doi.org/10.1016/j.compbiomed.2011.10.008.

  • 93. Cimen S Hoogendoorn C Morris PD Gunn J Frangi AF. Reconstruction of coronary trees from 3DRA using a 3D+t statistical cardiac prior. Med Image Comput Comput Assist Interv. 2014;17:619-626.

  • 94. Cimen S Gooya A Ravikumar N Taylor ZA Frangi AF. Reconstruction of coronary artery centrelines from X-ray angiography using a mixture of student’s t-distributions. Lecture Notes in Computer Science (MICCAI). 2016;9902:291-299. doi:

    • Crossref
    • Export Citation
  • 95. Hu Y Jung M Oukili A et al. Sparse reconstruction from a limited projection number of the coronary artery tree in X-ray rotational imaging. IEEE Int Symp Biomed Imag (ISBI). 2012; pp. 804-807. doi:

    • Crossref
    • Export Citation
  • 96. Fallavollita P Cheriet F. Optimal 3D reconstruction of coronary arteries for 3D clinical assessment. Comput Med Imaging Graph. 2008;32:476-487. doi:

    • Crossref
    • Export Citation
  • 97. Gülsün MA Funka-Lea G Sharma P Rapaka S Zheng YF. Coronary centerline extraction via optimal flow paths and CNN path pruning. Lecture Notes in Computer Science (MICCAI). 2016;9902:317-325. doi:

    • Crossref
    • Export Citation
  • 98. Kitamura Y Li YZ Ito W. Automatic coronary extraction by supervised detection and shape matching. IEEE Int Symp Biomed Imag (ISBI). 2012;234-237. doi:

    • Crossref
    • Export Citation
  • 99. Dufour A Tankyevych O Naegel B et al. Filtering and segmentation of 3D angiographic data: Advances based on mathematical morphology. Med Image Anal. 2013;17:147-164. http://dx.doi.org/10.1016/j.media.2012.08.004.

  • 100. Krissian K Carreira JM Esclarin J Maynar M. Semi-automatic segmentation and detection of aorta dissection wall in MDCT angiography. Med Image Anal. 2014;18:83-102. doi:

    • Crossref
    • Export Citation
  • 101. Shang YF Deklerck R Nyssen E et al. Vascular active contour for vessel tree segmentation. IEEE Trans Biomed Eng. 2011;58:1023-1032. doi:

    • Crossref
    • Export Citation
  • 102. Shin SY Lee S Noh KJ Yun ID Lee KM. Extraction of coronary vessels in fluoroscopic X-ray sequences using vessel correspondence optimization. Lecture Notes in Computer Science (MICCAI). 2016;9902:308-316. doi:

    • Crossref
    • Export Citation
  • 103. Liu L Shi WZ Rueckert D Hu MX Ourselin S Zhuang XH. Model-guided directional minimal path for fully automatic extraction of coronary centerlines from cardiac CTA. Med Image Comput Comput Assist Interv. 2013;16:542-549.

  • 104. Liu L Shi WZ Rueckert D Hu MX Ourselin S Zhuang XH. Coronary centerline extraction based on ostium detection and model-guided directional minimal path. IEEE Int Symp Biomed Imag (ISBI). 2014;133-136. doi:

    • Crossref
    • Export Citation
  • 105. Sun SY Wang P Sun SH Chen T. Model-guided extraction of coronary vessel structures in 2D X-ray angiograms. Med Image Comput Comput Assist Interv. 2014;17:594-602.

  • 106. Medrano-Garcia P Ormiston J Webster M et al. Construction of a coronary artery atlas from CT angiography. Med Image Comput Comput Assist Interv. 2014;17:513-520.

  • 107. Zheng YF Tek H Funka-Lea G. Robust and accurate coronary artery centerline extraction in CTA by combining modeldriven and data-driven approaches. Med Image Comput Comput Assist Interv. 2013;16:74-81.

  • 108. Perona P Malik J. Scale space and edge detection using anisotropic diffusion. IEEE Trans Patt Anal Mach Intell. 1990;12:629-639.

  • 109. Frangi AF Niessen WJ Vincken KL Viergever MA. Multiscale vessel enhancement filtering. Lecture Notes in Computer Science (MICCAI). 1998;1496:130-137.

  • 110. Han DJ Doan NT Shim H et al. A fast seed detection using local geometrical feature for automatic tracking of coronary arteries in CTA. Comput Methods Programs Biomed. 2014;117:179-188. doi:

    • Crossref
    • Export Citation
  • 111. Benyó B. Identification of dental root canals and their medial line from micro-CT and cone-beam CT records. BioMed Eng Online 2012;11:81. doi:

    • Crossref
    • Export Citation
  • 112. Au OKC Tai CL Chu HK Cohen-Or D Lee TY. Skeleton extraction by mesh contraction. ACM Trans Graph. 2008;27:1-10.

  • 113. Hoffmann U Bamberg F Chae CU et al. Coronary computed tomography angiography for early triage of patients with acute chest pain: the ROMICAT (Rule Out Myocardial Infarction using Computer Assisted Tomography) trial. J Am Coll Cardiol. 2009;53:1642-1650. doi:

    • Crossref
    • Export Citation
  • 114. Hoffman U Truong Q Schoenfield D et al. Coronary CT angiography versus Standard Evaluation in Acute Chest Pain. N Engl J Med. 2012;367:299-308. doi:

    • Crossref
    • Export Citation
  • 115. Kolansky DM. Acute coronary syndromes: morbidity mortality and pharmacoeconomic burden. Am J Manag Care. 2009;15:36-41.

  • 116. Hagan PG Nienaber CA Isselbacher EM et al. The International Registry of Acute Aortic Dissection (IRAD): new insights into an old disease. JAMA. 2000;283:897-903.

  • 117. Eagle KA Lim MJ Dabbous OH et al. A validated prediction model for all forms of acute coronary syndrome: estimating the risk of 6-month postdischarge death in an international registry. JAMA. 2004;9;291:2727-2733. doi:

    • Crossref
    • Export Citation
  • 118. Kristensen TS Kofoed KF Kühl JT Nielsen WB Nielsen MB Kelbæk HJ. Prognostic implications of non-obstructive coronary plaques in patients with non-ST-segment elevation myocardial infarction: a multidetector computed tomography study. J Am Coll Cardiol. 2011;58:502-509. doi:

    • Crossref
    • Export Citation
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