Lumen segmentation and stenosis quantification of atherosclerotic carotid arteries in CTA utilizing a centerline intensity prior

Medical Physics

Volumn 40, Issue 5, 2013, Pages

  Tang, Hui ^a,b   Van Walsum, Theo ^a   Hameeteman, Reinhard ^a   Shahzad, Rahil ^a,b   Van Vliet, Lucas J ^b   Niessen, Wiro J ^a,b  

^a ERASMUS MEDICAL CENTER   (Netherlands)

^b DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

calcium; envelope of the maximum inscribed spheres; geodesic active contour; gradient magnitude; regional intensity

Indexed keywords

CALCIUM; ITERATIVE METHODS;

CARDIO-VASCULAR DISEASE; CAROTID ARTERY STENOSIS; CROSS SECTIONAL DIAMETERS; EXTERNAL CAROTID ARTERIES; GEODESIC ACTIVE CONTOUR; GRADIENT MAGNITUDE; MAXIMUM INSCRIBED SPHERES; REGIONAL INTENSITY;

IMAGE SEGMENTATION;

CALCIUM;

ANALYTIC METHOD; ARTERY DIAMETER; ARTICLE; AUTOMATION; BLOOD VESSEL WALL; CARDIOVASCULAR PROCEDURE; CAROTID ARTERY BIFURCATION; CAROTID ARTERY OBSTRUCTION; CAROTID ARTERY STENOSIS QUANTIFICATION; CAROTID ATHEROSCLEROSIS; CAROTID LUMEN SEGMENTATION; COMPUTED TOMOGRAPHIC ANGIOGRAPHY; CONTROLLED STUDY; DISEASE CLASSIFICATION; EXTERNAL CAROTID ARTERY; HUMAN; INTERNAL CAROTID ARTERY; KERNEL METHOD; MEASUREMENT ACCURACY; PRIORITY JOURNAL; PROCESS OPTIMIZATION; THREE DIMENSIONAL IMAGING; ANGIOGRAPHY; ATHEROSCLEROSIS; CAROTID ARTERY; CAROTID ARTERY DISEASE; IMAGE PROCESSING; METHODOLOGY; RADIOGRAPHY; STENOSIS, OCCLUSION AND OBSTRUCTION; PROCEDURES;

ANGIOGRAPHY; ATHEROSCLEROSIS; CAROTID ARTERIES; CAROTID ARTERY DISEASES; CONSTRICTION, PATHOLOGIC; IMAGE PROCESSING, COMPUTER-ASSISTED;

EID: 84877291440     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.4802751     Document Type: Article

References (44)

1. S. Mendis, P. Puska, and B. Norrving, Global atlas on cardiovascular disease prevention and control., WHO Report ISBN: 978 92 4 156437 3, 2011.
2. M. J. Koelemay, P. J. Nederkoorn, J. B. Reitsma, and C. B. Majoie, Systematic review of computed tomographic angiography for assessment of carotid artery disease., Stroke 35, 2306-2312 (2004). 10.1161/01.STR.0000141426.63959.cc
3. H. J. Barnett, Benefit of carotid endarterectomy in patients with symptomatic moderate or severe stenosis., N. Engl. J. Med. 339 (20), 1415-1425 (1998). 10.1056/NEJM199811123392002
4. C. Warlow, MRC European carotid surgery trial: Interim results for symptomatic patients with severe (70-99) stenosis or with mild (0-29) carotid stenosis., Lancet 337, 1235-1243 (1991). 10.1016/0140-6736(91)92916-P
5. M. Walker, Endarterectomy for asymptomatic carotid artery stenosis., J. Am. Med. Assoc. 273 (18), 1421-1428 (1995). 10.1001/jama.1995.03520420037035
6. K. Hameeteman, Evaluation framework for carotid bifurcation lumen segmentation and stenosis grading., Med. Image Anal. 15, 477-488 (2011). 10.1016/j.media.2011.02.004
7. H. Scherl, J. Hornegger, M. Prümmer, and M. Lell, Semi-automatic level-set based segmentation and stenosis quantification of the internal carotid artery in 3D CTA data sets., Med. Image Anal. 11, 21-34 (2007). 10.1016/j.media.2006.09.004
8. M. A. Zuluaga, Adaptations of MARACAS algorithm to the segmentation of the carotid bifurcation and stenosis quantification in CTA images., in Proceedings of Carotid Lumen Segmentation and Stenosis Grading Challenge, London, UK, 2009.
9. B. M. Kelm, S. Mittal, Y. Zheng, A. Tsymbal, D. Bernhardt, F. Vega-Higuera, S. K. Zhou, P. Meer, and D. Comaniciu, Detection, grading and classification of coronary stenoses in computed tomography angiography., in Proceedings of Medical Image Computing and Computer-Assisted Intervention, Toronto, Canada, (Springer-Verlag, Springer, Berlin Heidelberg, 2011), Vol. 6893, pp. 25-32.
10. D. Lesage, E. D. Angelini, I. Bloch, and G. Funka-Lea, A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes., Med. Image Anal. 13 (6), 819-845 (2009). 10.1016/j.media.2009.07.011
11. T. F. Cootes, C. J. Taylor, D. H. Cooper, and J. Graham, Active shape models-their training and application., Comput. Vis. Image Underst. 61, 38-59 (1995). 10.1006/cviu.1995.1004
12. M. de Bruijne, B. van Bemmel, C. M. Ginneken, M. Viergever, and W. Niessen, Adapting active shape models for 3D segmentation of tubular structures in medical images., Inf. Process. Med. Imaging 2732, 136-147 (2003). 10.1007/978-3-540-45087-0-12
13. Y. Boykov and G. Funka-Lea, Graph cuts and efficient N-D image segmentation., Int. J. Comput. Vis. 70, 109-131 (2006). 10.1007/s11263-006-7934- 5
14. L. M. Lorigo, O. D. Faugeras, W. E. L. Grimson, R. Keriven, R. Kikinis, A. Nabavi, and C. F. Westin, Curves: Curve evolution for vessel segmentation., Med. Image Anal. 5, 195-206 (2001). 10.1016/S1361-8415(01)00040-8
15. T. F. Chan and L. A. Vese, Active contours without edges., IEEE Trans. Image Process. 10 (2), 266-277 (2001). 10.1109/83.902291
16. C. Li, R. Huang, Z. Ding, J. C. Gatenby, D. N. Metaxas, and J. C. Gore, A level set method for image segmentation in the presence of intensity inhomogeneities with application to MRI., IEEE Trans. Image Process. 20 (7), 2007-2016 (2011). 10.1109/TIP.2011.2146190
17. C. Li, C.-Y. Kao, J. C. Gore, and Z. Ding, Minimization of region-scalable fitting energy for image segmentation., IEEE Trans. Image Process. 17, 1940-1949 (2008). 10.1109/TIP.2008.2002304
18. S. Lankton and A. Tannenbaum, Localizing region-based active contours., IEEE Trans. Image Process. 17, 2029-2039 (2008). 10.1109/TIP.2008.2004611
19. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever, Multiscale vessel enhancement filtering., in Proceedings of Medical Image Computing and Computer-Asisted Intervention, Cambridge MA, (Springer, Berlin Heidelberg, 1998), Vol 1496, pp. 130-137.
20. Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images., Med. Image Anal. 2 (2), 143-168 (1998). 10.1016/S1361-8415(98)80009-1
21. M. A. Gülsün and H. Tek, Segmentation of carotid arteries by graph-cuts using centerline models., in Proceedings of Carotid Lumen Segmentation and Stenosis Grading Challenge, London, UK, 2009.
22. M. Schaap, L. Neefjes, C. Metz, A. van der Giessen, A. Weustink, N. Mollet, J. Wentzel, T. van Walsum, and W. Niessen, Coronary lumen segmentation using graph cuts and robust kernel regression., Inf. Process. Med. Imaging 5636, 528-539 (2009). 10.1007/978-3-642-02498-6-44
23. H. Homann, G. Vesom, and J. Noble, Vasculature segmentation of CT liver images using graph cuts and graph-based analysis., in Proceedings of IEEE International Symposium on Biomedical Imaging, Paris, France, (IEEE, 2008), pp. 53-56.
24. R. Manniesing, M. Schaap, S. Rozie, K. Hameeteman, D. Vukadinovic, A. van der Lugt, and W. Niessen, Robust CTA lumen segmentation of the atherosclerotic carotid artery bifurcation in a large patient population., Med. Image Anal. 14 (6), 759-769 (2010). 10.1016/j.media.2010.05.001
25. H. Tang, T. van Walsum, R. S. van Onkelen, K. Hameeteman, S. Klein, M. Schaap, Q. J. B. Bouwhuijsen, J. Witteman, A. van der Lugt, L. J. van Vliet, and W. Niessen, Semiautomatic carotid lumen segmentation for quantification of lumen geometry in multispectral MRI., Med. Image Anal. 16 (6), 1202-1215 (2012). 10.1016/j.media.2012.05.014
26. K. Krissian and S. A. García, A minimal cost path and level set evolution approach for carotid bifurcation segmentation., in Proceedings of MICCAI 2008 Workshop on Carotid Lumen Segmentation and Stenosis Grading Challenge, London, UK, 2009.
27. E. Ukwatta, J. Awad, A. Ward, D. Buchanan, J. Samarabandu, G. Parraga, and A. Fenster, Three-dimensional ultrasound of carotid atherosclerosis: Semiautomated segmentation using a level set-based method., Med. Phys. 38, 2479 (2011). 10.1118/1.3574887
28. Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts., IEEE Trans. Pattern Anal. Mach. Intell. 23, 1222-1239 (2001). 10.1109/34.969114
29. H. Tang, T. van Walsum, K. Hameeteman, M. Schaap, A. van der Lugt, L. J. van Vliet, and W. Niessen, Lumen segmentation of atherosclerotic carotid arteries in CTA., in Proceedings of IEEE International Symposium on Biomedical Imaging, Barcelona, Spain, (IEEE, 2012), pp. 274-277.
30. M. Schaap, T. van Walsum, L. Neefjes, C. Metz, E. Capuano, M. de Bruijne, and W. Niessen, Robust shape regression for supervised vessel segmentation and its application to coronary segmentation in CTA., IEEE Trans. Med. Imaging 30 (11), 1974-1986 (2011). 10.1109/TMI.2011.2160556
31. R. Manniesing and W. Niessen, Shape constrained vessel centerline extraction by integrating surface evolution and topology analysis., in Proceedings of IEEE International Symposium on Biomedical Imaging, Arlington, VA, (IEEE, 2006), pp. 165-168.
32. O. Cuisenaire, Fully automated segmentation of carotid and vertebral arteries from CTA., in Proceedings of Carotid Lumen Segmentation and Stenosis Grading Challenge, London, UK, 2009.
33. C. van Wijk, V. van Ravesteijn, F. Vos, and L. van Vliet, Detection and segmentation of colonic polyps on implicit isosurfaces by second principal curvature flow., IEEE Trans. Med. Imaging 29, 688-698 (2010). 10.1109/TMI.2009.2031323
34. R. Malladi and J. A. Sethlan, Image processing via level set curvature flow., Appl. Math. 92, 7046-7050 (1995).
35. V. Caselles, R. Kimmel, and G. Sapiro, Geodesic active contours., Int. J. Comput. Vis. 22 (1), 61-79 (1997). 10.1023/A:1007979827043
36. E. W. Dijkstra, A note on two problems in connexion with graphs., Numer. Math. 1 (1), 269-271 (1959). 10.1007/BF01386390
37. M. A. Gülsün and H. Tek, Robust vessel tree modeling., in Proceedings of Medical Image Computing and Computer-Assisted Intervention, New York, (Springer, Berlin Heidelberg, 2008), Vol. 5241, pp. 602-611.
38. R. van Heekeren, F. Faas, and L. van Vliet, Finding the minimum-cost path without cutting corners., Image Anal. 4522, 263-272 (2007). 10.1007/978-3-540-73040-8-27
39. V. Caselles, R. Kimmel, G. Sapiro, and C. Sbert, Minimal surfaces: A geometric three dimensional segmentation approach., Numer. Math. 77, 423-451 (1997). 10.1007/s002110050294
40. S. Osher and J. A. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on hamilton-jacobi formulations., J. Comput. Phys. 79, 12-49 (1988). 10.1016/0021-9991(88)90002-2
41. L. Antiga and D. A. Steinman, Robust and objective decomposition and mapping of bifurcating vessels., IEEE Trans. Med. Imaging 23 (6), 704-713 (2004). 10.1109/TMI.2004.826946
42. K. Hameeteman, M. Freiman, M. Zuluaga, L. Joskowicz, S. Rozie, M. van Gils, L. van den Borne, J. Sosna, P. Berman, N. Cohen, P. Douek, I. Sánchez, M. Aissat, A. van der Lugt, G. P. Krestin, W. Niessen, and T. van Walsum, 3D segmentation in the clinic: A grand challenge III., in Proceedings of MICCAI 2009 Workshop on Carotid Lumen Segmentation and Stenosis Grading Challenge, London, UK, 2009.
43. R. T. Whitaker, A level-set approach to 3D reconstruction from range data., Int. J. Comput. Vis. 29, 203-231 (1998). 10.1023/A:1008036829907
44. L. R. Dice, Measures of the amount of ecologic association between species., Ecology 26 (3), 297-302 (1945). 10.2307/1932409