A Framework of Wire Tracking in Image Guided Interventions

Part of the Advances in Pattern Recognition book series (ACVPR)


This chapter presents a framework of using computer vision and machine learning methods to tracking guidewire, a medical device inserted into vessels during image guided interventions. During interventions, the guidewire exhibits nonrigid deformation due to patients’ breathing and cardiac motions. Such 3D motions are complicated when being projected onto the 2D fluoroscopy. Furthermore, fluoroscopic images have severe image artifacts and other wire-like structures. Those factors make robust guidewire tracking a challenging problem. To address these challenges, this chapter presents a probabilistic framework for the purpose of robust tracking. We introduce a semantic guidewire model that contains three parts, including a catheter tip, a guidewire tip and a guidewire body. Measurements of different parts are integrated into a Bayesian framework as measurements of a whole guidewire for robust guidewire tracking. For each part, two types of measurements, one from learning-based detectors and the other from appearance models, are combined. A hierarchical and multi-resolution tracking scheme based on kernel-based measurement smoothing is then developed to track guidewires effectively and efficiently in a coarse-to-fine manner. The framework has been validated on a testing set containing 47 sequences acquired under clinical environments, and achieves a mean tracking error of less than 2 pixels.


Control Point Measurement Model Tracking Error Fluoroscopic Image Tracking Precision 
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  1. 1.
    Avidan, S.: Ensemble tracking. In: CVPR, pp. 130–136 (2005) Google Scholar
  2. 2.
    Baert, S.A.M., Viergever, M.A., Niessen, W.J.: Guide wire tracking during endovascular interventions. IEEE Trans. Med. Imaging 22(8), 965–972 (2003) CrossRefGoogle Scholar
  3. 3.
    Baim, D.S.: Cardiac Catheterization, Angiography, and Intervention, 7th edn. Lippincott William’s and Wilkins, Philadelphia (2006) Google Scholar
  4. 4.
    Barbu, A., Athitsos, V., Georgescu, B., Boehm, S., Durlak, P., Comaniciu, D.: Hierarchical learning of curves application to guidewire localization in fluoroscopy. In: CVPR (2007) Google Scholar
  5. 5.
    Bartels, R.H., Beatty, J.C., Barsky, B.A.: An Introduction to Splines for Use in Computer Graphics and Geometric Modelling. Morgan Kaufmann, San Mateo (1998) Google Scholar
  6. 6.
    Carneiro, G., Georgescu, B., Good, S., Comaniciu, D.: Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree. IEEE Trans. Med. Imaging 27(9), 1342–1355 (2008) CrossRefGoogle Scholar
  7. 7.
    Friedman, J., Hastie, T., Tibshirani, R.: Additive logistic regression: a statistical view of boosting. Ann. Stat. 28(2), 337–374 (2000) MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Isard, M., Blake, A.: Condensation: conditional density propagation for visual tracking. Int. J. Comput. Vis. 29(1), 5–28 (1998) CrossRefGoogle Scholar
  9. 9.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–331 (1987) CrossRefGoogle Scholar
  10. 10.
    Mazouer, P., Chen, T., Zhu, Y., Wang, P., Durlak, P., Thiran, J.-P., Comaniciu, D.: User-constrained guidewire localization in fluoroscopy. In: Medical Imaging: Image Processing. Proc. SPIE. SPIE, Bellingham (2009) Google Scholar
  11. 11.
    Palti-Wasserman, D., Brukstein, A.M., Beyar, R.: Identifying and tracking a guide wire in the coronary arteriesduring angioplasty from x-ray images. IEEE Trans. Biomed. Eng. 44(2), 152–164 (1997) CrossRefGoogle Scholar
  12. 12.
    Peters, T., Cleary, K.: Image-Guided Interventions: Technology and Applications. Springer, Berlin (2008) CrossRefGoogle Scholar
  13. 13.
    Staib, L., Duncan, J.: Boundary finding with parametrically deformable models. IEEE Trans. Pattern Anal. Mach. Intell. 14, 1061–1075 (1992) CrossRefGoogle Scholar
  14. 14.
    Tu, Z.: Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589–1596 (2005) Google Scholar
  15. 15.
    Viola, P., Jones, M.: Robust real-time object detection. Int. J. Comput. Vis. 57(2), 137–154 (2004) CrossRefGoogle Scholar
  16. 16.
    Wang, P., Ji, Q.: Robust face tracking via collaboration of generic and specific models. IEEE Trans. Image Process. 17(7), 1189–1199 (2008) MathSciNetCrossRefGoogle Scholar
  17. 17.
    Wang, P., Chen, T., Zhu, Y., Zhang, W., Zhou, S., Comaniciu, D.: Robust guidewire tracking in fluoroscopy. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 691–698 (2009) Google Scholar
  18. 18.
    Wang, P., Zhu, Y., Zhang, W., Chen, T., Durlak, P., Bill, U., Comaniciu, D.: Hierarchical guidewire tracking in fluoroscopic sequences. In: SPIE: Medical Imaging, vol. 7258, pp. 72591L–72591L–8. SPIE, Bellingham (2009) Google Scholar
  19. 19.
    Wu, Y., Huang, T.S.: Robust visual tracking by integrating multiple cues based on co-inference learning. Int. J. Comput. Vis. 58(1), 55–71 (2004) CrossRefGoogle Scholar
  20. 20.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2006) CrossRefGoogle Scholar
  21. 21.
    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. Med. Imaging 27(11), 1668–1681 (2008) CrossRefGoogle Scholar
  22. 22.
    Zhou, X., Comaniciu, D., Gupta, A.: An information fusion framework for robust shape tracking. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 115–129 (2005) CrossRefGoogle Scholar
  23. 23.
    Zhu, S.C., Yuille, A.L.: Forms: a flexible object recognition and modeling system. Int. J. Comput. Vis. 20, 187–212 (1996) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Siemens Corporate ResearchSiemens CorporationPrincetonUSA

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