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Stent Shape Estimation through a Comprehensive Interpretation of Intravascular Ultrasound Images

  • Francesco Ciompi
  • Simone Balocco
  • Carles Caus
  • Josepa Mauri
  • Petia Radeva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8150)

Abstract

We present a method for automatic struts detection and stent shape estimation in cross-sectional intravascular ultrasound images. A stent shape is first estimated through a comprehensive interpretation of the vessel morphology, performed using a supervised context-aware multi-class classification scheme. Then, the successive strut identification exploits both local appearance and the defined stent shape. The method is tested on 589 images obtained from 80 patients, achieving a F-measure of 74.1% and an averaged distance between manual and automatic struts of 0.10 mm.

Keywords

IVUS Stent detection Stacked Sequential Learning 

References

  1. 1.
    Yoon, H.J., Hur, S.H.: Optimization of stent deployment by intravascular ultrasound. Korean J. Intern. Med. 27(1), 30–38 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Canero, C., Pujol, O., Radeva, P., Toledo, R., Saludes, J., Gil, D., Villanueva, J., Mauri, J., Garcia, B., Gomez, J.: Optimal stent implantation: three-dimensional evaluation of the mutual position of stent and vessel via intracoronary echocardiography. In: Computers in Cardiology, pp. 261–264 (1999)Google Scholar
  3. 3.
    Dijkstra, J., Koning, G., Tuinenburg, J., Oemrawsingh, P.V., Reiber, J.: Automatic border detection in intravascular iltrasound images for quantitative measurements of the vessel, lumen and stent parameters. Computers in Cardiology 28 (Cat. No.01CH37287) 1230, 25–28 (2001)Google Scholar
  4. 4.
    Dijkstra, J., Koning, G., Tuinenburg, J., Oemrawsingh, P.V., Reiber, J.: Automatic stent border detection in intravascular ultrasound images. In: CARS, pp. 1111–1116 (2003)Google Scholar
  5. 5.
    Rotger, D., Radeva, P., Bruining, N.: Automatic detection of bioabsorbable coronary stents in ivus images using a cascade of classifiers. IEEE Transactions on Information Technology in Biomedicine 14(2), 535–537 (2010)CrossRefGoogle Scholar
  6. 6.
    Hua, R., Pujol, O., Ciompi, F., Balocco, S., Alberti, M., Mauri, F., Radeva, P.: Stent strut detection by classifying a wide set of ivus features. In: MICCAI Workshop on Computer Assisted Stenting (2012)Google Scholar
  7. 7.
    Ciompi, F., Pujol, O., Gatta, C., Alberti, M., Balocco, S., Carrillo, X., Mauri-Ferre, J., Radeva, P.: Holimab: A holistic approach for media-adventitia border detection in intravascular ultrasound. In: Medical Image Analysis, vol. 16, pp. 1085–1100 (2012)Google Scholar
  8. 8.
    Lagarias, J.C., Reeds, J.A., Wright, M.H., Wright, P.E.: Convergence Properties of the Nelder-Mead Simplex Method in Low Dimensions. SIAM Journal of Optimization 9(1), 112–147 (1998)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francesco Ciompi
    • 1
    • 2
  • Simone Balocco
    • 1
    • 2
  • Carles Caus
    • 3
  • Josepa Mauri
    • 3
  • Petia Radeva
    • 1
    • 2
  1. 1.Dep. of Applied Mathematics and AnalysisUniversity of BarcelonaSpain
  2. 2.Computer Vision CenterCampus UABBarcelonaSpain
  3. 3.Hospital Universitari “Germans Trias i Pujol”BadalonaSpain

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