Advertisement

Real-Time Guiding Catheter and Guidewire Detection for Congenital Cardiovascular Interventions

  • YingLiang MaEmail author
  • Mazen Alhrishy
  • Maria Panayiotou
  • Srinivas Ananth Narayan
  • Ansab Fazili
  • Peter Mountney
  • Kawal S. Rhode
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10263)

Abstract

Guiding catheters and guidewires are used extensively in pediatric cardiac catheterization procedures for congenital heart diseases (CHD). Detecting their positions in fluoroscopic X-ray images is important for several clinical applications, such as visibility enhancement for low dose X-ray images, and co-registration between 2D and 3D imaging modalities. As guiding catheters are made from thin plastic tubes, they can be deformed by cardiac and breathing motions. Therefore, detection is the essential step before automatic tracking of guiding catheters in live X-ray fluoroscopic images. However, there are several wire-like artifacts existing in X-ray images, which makes developing a real-time robust detection method very challenging. To solve those challenges in real-time, a localized machine learning algorithm is built to distinguish between guiding catheters and artifacts. As the machine learning algorithm is only applied to potential wire-like objects, which are obtained from vessel enhancement filters, the detection method is fast enough to be used in real-time applications. The other challenge is the low contrast between guiding catheters and background, as the majority of X-ray images are low dose. Therefore, the guiding catheter might be detected as a discontinuous curve object, such as a few disconnected line blocks from the vessel enhancement filter. A minimum energy method is developed to trace the whole wire object. Finally, the proposed methods are tested on 1102 images which are from 8 image sequences acquired from 3 clinical cases. Results show an accuracy of 0.87 ± 0.53 mm which is measured as the error distances between the detected object and the manually annotated object. The success rate of detection is 83.4%.

Keywords

Target Object Image Artifact Image Mask Line Block Vessel Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Yilmaz, A., Javed, O., Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 13 (2006)CrossRefGoogle Scholar
  2. 2.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vision 1(4), 321–331 (1987)CrossRefGoogle Scholar
  3. 3.
    Zhu, S.C., Yuille, A.L.: Forms: a flexible object recognition and modeling system. Int. J. Comput. Vision 20, 187–212 (1996)CrossRefGoogle Scholar
  4. 4.
    Palti-Wasserman, D., Brukstein, A.M., Beyar, R.: Identifying and tracking a guide wire in the coronary arteries during angioplasty from x-ray images. IEEE Trans. Biomed. Eng. 44(2), 152–164 (1997)CrossRefGoogle Scholar
  5. 5.
    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)CrossRefzbMATHGoogle Scholar
  6. 6.
    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
  7. 7.
    Wang, P., Chen, T., Zhu, Y., Zhang, W., Zhou, S.K., Comaniciu, D.: Robust guidewire tracking in fluoroscopy. In: CVPR (2009)Google Scholar
  8. 8.
    Pauly, O., Heibel, H., Navab, N.: A machine learning approach for deformable guide-wire tracking in fluoroscopic sequences. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 343–350. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15711-0_43 CrossRefGoogle Scholar
  9. 9.
    Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998). doi: 10.1007/BFb0056195 CrossRefGoogle Scholar
  10. 10.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  11. 11.
    Fazlali, H.R., et al.: Vessel region detection in coronary X-ray angiograms. In: International Conference on Image Processing (2015)Google Scholar
  12. 12.
    Ma, Y.L., Gogin, N., Cathier, P., Housden, R.J., Gijsbers, G., Cooklin, M., O’Neill, M., Gill, J., Rinaldi, C.A., Razavi, R., Rhode, K.S.: Real-time x-ray fluoroscopy-based catheter detection and tracking for cardiac electrophysiology interventions. Med. Phys. 40(7), 071902 (2013)CrossRefGoogle Scholar
  13. 13.
    Hecht, E.: Optics, 4th edn. Addison Wesley, San Francisco (2002)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • YingLiang Ma
    • 1
    • 2
    Email author
  • Mazen Alhrishy
    • 3
  • Maria Panayiotou
    • 3
  • Srinivas Ananth Narayan
    • 4
  • Ansab Fazili
    • 3
  • Peter Mountney
    • 5
  • Kawal S. Rhode
    • 3
  1. 1.School of Computing, Electronics and MathematicsCoventry UniversityCoventryUK
  2. 2.School of Computing and Digital TechnologyBirmingham City UniversityBirminghamUK
  3. 3.Division of Imaging Sciences and Biomedical EngineeringKing’s College LondonLondonUK
  4. 4.Department of CardiologyGuy’s and St. Thomas’ Hospitals NHS Foundation TrustLondonUK
  5. 5.Medical Imaging TechnologiesSiemens HealthineersPrincetonUSA

Personalised recommendations