Learning-based hypothesis fusion for robust catheter tracking in 2D X-ray fluoroscopy

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2011, Pages 1097-1104

  Wu, Wen ^a   Chen, Terrence ^a   Barbu, Adrian ^b   Wang, Peng ^a   Strobel, Norbert ^c   Zhou, Shaohua Kevin ^a   Comaniciu, Dorin ^a  

^a SIEMENS CORPORATE RESEARCH   (United States)

^b FLORIDA STATE UNIVERSITY   (United States)

^c SIEMENS AG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ABLATION; DEFORMATION; ERRORS; PATTERN RECOGNITION;

ABLATION CATHETERS; ANATOMICAL MODELING; ATRIAL FIBRILLATION; BAYESIAN FRAMEWORKS; FRAMES PER SECONDS; NON-RIGID DEFORMATION; QUANTITATIVE EVALUATION; REAL-TIME NAVIGATION;

CATHETERS;

EID: 80052908797     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2011.5995553     Document Type: Conference Paper

References (16)

1. S. Avidan. Ensemble tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007. 1098
2. B. Babenko, M.-H. Yang, and S. Belongie. Visual tracking with online multiple instance learning. In CVPR, 2009. 1098
3. A. Barbu, V. Athitsos, B. Georgescu, S. Boehm, P. Durlak, and D. Comaniciu. Hierarchical learning of curves application to guidewire localization in fluoroscopy. In CVPR, 2007. 1098
4. H. Calkins and et al. HRS/EHRA/ECAS expert consensus statement on catheter and surgical ablation of atrial fibrillation: recommendations for personnel, policy, procedures and follow-up. a report of the heart rhythm society (HRS) task force on catheter and surgical ablation of atrial fibrillation. Heart Rhythm, 4, 2007. 1097
5. D. Comaniciu, V. Ramesh, and P. Meer. Real-time tracking of nonrigid objects using mean shift. In CVPR, 2000. 1100
6. D. Comaniciu, X. Zhou, and S. Krishnan. Robust realtime tracking of myocardial border: An information fusion approach. IEEE Transactions on Medical Imaging, 2008. 1100
7. H. Grabner and H. Bischof. On-line boosting and vision. In CVPR, 2006. 1098
8. H. U. Klemm and et al. Catheter motion during atrial ablation due to the beating heart and respiration: Impact on accuracy and spatial referencing in three-dimensional mapping. Heart Rhythm, 2007. 1097, 1098
9. Y. Ma, A. P. King, N. Gogin, C. A. Rinaldi, J. Gill, R. Razavi, and K. S. Rhode. Real-time respiratory motion correction for cardiac electrophysiology procedures using image-based coronary sinus catheter tracking. In International Conference on Medical Image Computing and Computer Assisted Intervention, 2010. 1098
10. J. Pilet, V. Lepetit, and P. Fua. Real-time non-rigid surface detection. In CVPR, 2005. 1098
11. M. Powell. An efficient method for finding the minimum of a function of several variables without calculating derivatives. Computer Journal, 1964. 1101
12. Z. Tu. Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In ICCV, 2005. 1098, 1099
13. P. Viola and M. J. Jones. Robust real-time face detection. International Journal of Computer Vision, 2004. 1098
14. P. Wang, T. Chen, Y. Zhu, W. Zhang, S. K. Zhou, and D. Comaniciu. Robust guidewire tracking in fluoroscopy. In CVPR, 2009. 1098, 1100, 1101
15. Y. Wang, B. Georgescu, D. Comaniciu, and H. Houle. Learning-based 3D myocardial motion flow estimation using high frame rate volumetric ultrasound data. In IEEE International Symposium on Biomedical Imaging, 2010. 1100
16. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering, and D. Comaniciu. Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Transactions on Medical Imaging, 2008. 1099