Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

International Conference on Medical Image Computing and Computer-Assisted Intervention

MICCAI 2012: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012 pp 17–24Cite as

  1. Home
  2. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012
  3. Conference paper
Catheter Tracking via Online Learning for Dynamic Motion Compensation in Transcatheter Aortic Valve Implantation

Catheter Tracking via Online Learning for Dynamic Motion Compensation in Transcatheter Aortic Valve Implantation

  • Peng Wang19,
  • Yefeng Zheng19,
  • Matthias John20 &
  • …
  • Dorin Comaniciu19 
  • Conference paper
  • 4050 Accesses

  • 5 Citations

Part of the Lecture Notes in Computer Science book series (LNIP,volume 7511)

Abstract

Dynamic overlay of 3D models onto 2D X-ray images has important applications in image guided interventions. In this paper, we present a novel catheter tracking for motion compensation in the Transcatheter Aortic Valve Implantation(TAVI). To address such challenges as catheter shape and appearance changes, occlusions, and distractions from cluttered backgrounds, we present an adaptive linear discriminant learning method to build a measurement model online to distinguish catheters from background. An analytic solution is developed to effectively and efficiently update the discriminant model and to minimize the classification errors between the tracking object and backgrounds. The online learned discriminant model is further combined with an offline learned detector and robust template matching in a Bayesian tracking framework. Quantitative evaluations demonstrate the advantages of this method over current state-of-the-art tracking methods in tracking catheters for clinical applications.

Keywords

  • Linear Discriminant Analysis
  • Transcatheter Aorta Valve Implantation
  • Pigtail Catheter
  • Multiple Instance Learning
  • Fisher Discriminant Analysis

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.

Download conference paper PDF

References

  1. Babenko, B., Yang, M.H., Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)

    Google Scholar 

  2. Brost, A., Liao, R., Hornegger, J., Strobel, N.: 3-D Respiratory Motion Compensation during EP Procedures by Image-Based 3-D Lasso Catheter Model Generation and Tracking. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 394–401. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  3. Collins, R., Liu, Y., Leordeanu, M.: Online selection of discriminative tracking features. IEEE Trans. on PAMI 27(10), 1631–1643 (2005)

    CrossRef  Google Scholar 

  4. Grabner, M., Grabner, H., Bischof, H.: Learning features for tracking. In: CVPR (2007)

    Google Scholar 

  5. Karar, M.E., John, M., Holzhey, D., Falk, V., Mohr, F.-W., Burgert, O.: Model-Updated Image-Guided Minimally Invasive Off-Pump Transcatheter Aortic Valve Implantation. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part I. LNCS, vol. 6891, pp. 275–282. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  6. Lin, R.S., Yang, M.H., Levinson, S.: Object tracking using incremental Fisher discriminant analysis. In: ICPR, vol. 2, pp. 757–760 (2004)

    Google Scholar 

  7. Matthews, I., Ishikawa, T., Baker, S.: The template update problem. IEEE Trans. on Pattern Analysis and Machine Intelligence 26(6), 810–815 (2004)

    CrossRef  Google Scholar 

  8. Ross, D., Lim, J., Lin, R.S., Yang., M.H.: Incremental learning for robust visual tracking. International Journal of Computer Vision Special Issue: Learning for Vision (2007)

    Google Scholar 

  9. Tu, Z.: Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering. In: ICCV, pp. 1589–1596 (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Corporate Research and Technology, Siemens Corporation, Princeton, NJ, U.S.A.

    Peng Wang, Yefeng Zheng & Dorin Comaniciu

  2. Siemens AG, Healthcare Sector, Siemensstr. 1, Forchheim, Germany

    Matthias John

Authors
  1. Peng Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yefeng Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Matthias John
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dorin Comaniciu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Project Team Asclepios, Inria Sophia Antipolis, 06902, Sophia-Antipolis, France

    Nicholas Ayache & Hervé Delingette & 

  2. MIT, CSAIL, 02139, Cambridge, MA, USA

    Polina Golland

  3. Information and Communication Headquarters, Nagoya University, 464-8603, Nagoya, Japan

    Kensaku Mori

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wang, P., Zheng, Y., John, M., Comaniciu, D. (2012). Catheter Tracking via Online Learning for Dynamic Motion Compensation in Transcatheter Aortic Valve Implantation. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_3

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-33418-4_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33417-7

  • Online ISBN: 978-3-642-33418-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature