Efficient detection of native and bypass coronary ostia in cardiac CT volumes: Anatomical vs. pathological structures

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6893 LNCS, Issue PART 3, 2011, Pages 403-410

  Zheng, Yefeng ^a   Tek, Huseyin ^a   Funka Lea, Gareth ^a   Zhou, S Kevin ^a   Vega Higuera, Fernando ^b   Comaniciu, Dorin ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANATOMICAL STRUCTURES; AUTOMATIC DETECTION; CARDIAC CT; CARDIAC-COMPUTED TOMOGRAPHY; COMPUTATIONALLY EFFICIENT; CORONARY ARTERY DISEASE; CORONARY ARTERY SEGMENTATION; DETECTION METHODS; EFFICIENT DETECTION; FALSE POSITIVE DETECTION; IMAGE FEATURES; NON-INVASIVE IMAGING; PATHOLOGICAL STRUCTURES; PRIOR DISTRIBUTION; SEARCHING SPACES; SEARCHING STRATEGY; SEED POINT; TRAINING SETS;

BLOOD VESSELS; DETECTORS; DISEASES; GRAFTING (CHEMICAL); HEART; MEDICAL COMPUTING; MEDICAL IMAGING; TOMOGRAPHY;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; AORTA; ARTICLE; AUTOMATED PATTERN RECOGNITION; CARDIOLOGY; CARDIOVASCULAR DISEASE; COMPUTER ASSISTED TOMOGRAPHY; CORONARY ARTERY BYPASS GRAFT; CORONARY ARTERY DISEASE; CORONARY BLOOD VESSEL; DIAGNOSTIC IMAGING; HEART VOLUME; HUMAN; IMAGE PROCESSING; METHODOLOGY; PATHOLOGY; STATISTICAL MODEL; THREE DIMENSIONAL IMAGING; TIME;

ALGORITHMS; AORTA; CARDIAC VOLUME; CARDIOLOGY; CARDIOVASCULAR DISEASES; CORONARY ARTERY BYPASS; CORONARY DISEASE; CORONARY VESSELS; DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; TIME FACTORS; TOMOGRAPHY, X-RAY COMPUTED;

EID: 80053484018     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23626-6_50     Document Type: Conference Paper

References (9)

1. Lloyd-Jones, D., Adams, R., Carnethon, M., et al.: Heart disease and stroke statistics - 2009 update. Circulation 119(3), 21-181 (2009)
2. Metz, C., Schaap, M., van Walsum, T., van der Giessen, A., Weustink, A., Mollet, N., Krestin, G., Niessen, W.: 3D segmentation in the clinic: A grand challenge II - coronary artery tracking. In: Proc. MICCAI Workshop on 3D Segmentation in the Clinic: A Grand Challenge (2008)
3. Hennemuth, A., Boskamp, T., Fritz, D., Kühnel, C., Bock, S., Rinck, D., Scheuering, M., Peitgen, H.O.: One-click coronary tree segmentation in CT angiographic images. Computer Assisted Radiology and Surgery, 317-321 (2005)
4. Wang, C., Smedby, O.: An automatic seeding method for coronary artery segmentation and skeletonization in CTA. The Insight Journal (2008)
5. Tek, H., Gulsun, M.A., Laguitton, S., Grady, L., Lesage, D., Funka-Lea, G.: Automatic coronary tree modeling. The Insight Journal (2008)
6. Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Medical Imaging 27(11), 1668-1681 (2008)
7. Zheng, Y., John, M., Liao, R., Boese, J., Kirschstein, U., Georgescu, B., Zhou, S.K., Kempfert, J., Walther, T., Brockmann, G., Comaniciu, D.: Automatic aorta segmentation and valve landmark detection in C-arm CT: Application to aortic valve implantation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 476-483. Springer, Heidelberg (2010)
8. Tu, Z.: Probabilistic boosting-tree: Learning discriminative methods for classification, recognition, and clustering. In: Proc. Int'l. Conf. Computer Vision, pp. 1589-1596 (2005)
9. Liu, G., Haralick, R.M.: Optimal matching problem in detection and recognition performance evaluation. Pattern Recognition 35(3), 2125-2139 (2002)