Advanced SearchSearch Tips
Performance evaluation of vessel extraction algorithm applied to Aortic root segmentation in CT Angiography
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Performance evaluation of vessel extraction algorithm applied to Aortic root segmentation in CT Angiography
Kim, Tae-Hyong; Hwang, Young-sang; Shin, Ki-Young;
  PDF(new window)
World Health Organization reported that heart-related diseases such as coronary artery stenoses show the highest occurrence rate which may cause heart attack. Using Computed Tomography angiography images will allow radiologists to detect and have intervention by creating 3D roadmapping of the vessels. However, it is often complex and difficult do reconstruct 3D vessel which causes very large amount of time and previous researches were studied to segment vessels more accurate automatically. Therefore, in this paper, Region Competition, Geodesic Active Contour (GAC), Multi-atlas based segmentation and Active Shape Model algorithms were applied to segment aortic root from CTA images and the results were analyzed by using mean Hausdorff distance, volume to volume measure, computational time, user-interaction and coronary ostium detection rate. As a result, Extracted 3D aortic model using GAC showed the highest accuracy but also showed highest user-interaction results. Therefore, it is important to improve automatic segmentation algorithm in future
Aortic root;Computer-aided detection (CAD);Computed tomography angiography (CTA);Three-dimensional vessel segmentation;Geodesic Active Contour (GAC);
 Cited by
J. S. Yoon, T. S. Choi, "3D mechanical model based pulmonary nodule segmentation in CT images", 한국정보전자통신기술학회논문지, Vol. 8, No. 4, pp.319-326. 2015.

P. A. Yushkevich, J. Piven, H. C. Hazlett, R. G. Smith, S. Ho, J. C. Gee, G. Gerig, "User-guided 3D active contour segmentation of anatomical structurese: Significantly improved efficiency and reliability, NeuroImage, Vol. 31, No. 3, pp. 1116-1128, 2006. crossref(new window)

D. Lesage, E. D. Angelini, I. Bloch, G. Flunka-lea, "A review of 3D vessel lumen segmentation techniques: Models, features, and extraction schemes", Medical image analysis, Vol. 13, No. 6, pp. 819-845, 2009. crossref(new window)

K. Krissian, H. Bogunovic, J. M. Pozo, M. C. Villa-Uriol, A. F. Frangi, "Minimally Interactive Knowledge-based Coronary Tracking in CTA using a Minimal Cost Path", In 2008 MICCAI Workshop-Grand Challenge Coronary Artery Tracking. The Midas Journal, 2008

I. Waechter, R. Kneser, G. Korosoglou, J. Peters, N. H. Bakker, R. V. D. Boomen, J. Weese, "Patient Specific Models for Planning and Guidance of Minimally Invasive Aortic Valve Implantation", Medical Image Computing and Computer-Assisted Intervention-MICCAI 2010, pp. 526-533, 2010.

C. Kirbas, F. Quek, "A review of vessel extraction techniques and algorithms", ACM Computing Surveys (CSUR), Vol. 36, No. 2, pp. 81-121, 2004.

M. Schaap, C. T. Metz, T. V. Walsum, A. G. Giessen, A. C. Weustink, N. R. Mollet, C. Bauer, H. Bogunovic, C. Castro, X. Deng, E. Dikici, T. O'Donell, M. Frenay, O. Friman, M. H. Hoyos, P. H. Kitslaar, K. Krissian, C. Kuhnel, M. A. Leungo-oroz, M. Orkisz, O. Smedby, M. Styner, A. Szymczak, H. Tek, C. Wang, S. K. Warfield, S. Zambal, Y. Zhang, G. P. Krestin. W. J. Niessen, "Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms", Medical Image Analysis, Vol 13. pp. 701-714, 2009. crossref(new window)

X. G, Y. S, X. Li, D. Tao, "A review of Active Appearance Models", Systems, Man, and Cybermetics, Part C: Applications and Reviews, IEEE Transactins on, Vol. 40, No. 2, pp. 145-158, 2010.

Y. Zheng, M. John, R. Lao, J. Boese, U. Kirschstein, B. Georgescu, S. K. Zhou, J. Kempfert, T. Walther, G. Brockmann, D. Comaniciu, "Automatic Aorta segmentation and Valve Landmark Detection in C-arm CT:Application to Aortic Valve Implantation, Medical Imaging, IEEE Transactions on, Vol. 31, No. 12, pp. 2307-2321, 2012. crossref(new window)

F. Zhao, H. Zhang, A. Wahle, T. D. Scholz, M. Sonka, "Automated 4D segmentation of Aortic Magnetic Resonance Images", In BWMVC, pp. 247-256, 2006.

M. A. Elattar, E. M. Wiegerinck, R. N. Planken, E. Vanbavel, H. C. Van Assen, J. B jr, H. A. Marquering, "Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation", Medical & biological engineering & computing, Vol. 52, No. 7, pp. 611-618, 2014. crossref(new window)

O. Friman, C. Kuhnel, H. O. Peitgen, "Coronary Centerline Extraction Using Multiple Hypothesis Tracking and Minimal Paths", In: Proc MICCAI, 2008.

S. C. Zhu, A. Yuille, "Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation", IEEE Transactions on pattern analysis and machine intelligence, Vol 18, No. 9, pp. 884-900, 1996. crossref(new window)

A. Dopfer, H. H. Wang, C. C. Wang, "3D Active Appearance Model Alignment using intensity and range data", Robotics and Autonomous Systems, Vol. 62, No. 2, pp. 168-176, 2014. crossref(new window)

H. A. Kirisli, M. Schapp, S. Klein, "Evaluation of a multi-atlas based method for segmentation of cardiac CTA data: a large-scale, multicenter, and multivendor study", Medical physics, Vol. 37, No. 12, 2010.