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VOIDD: Automatic Vessel-of-Intervention Dynamic Detection in PCI Procedures

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 10552)

Abstract

In this article, we present the work towards improving the overall workflow of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the imaging instruments to precisely monitor the steps of the procedure. In the long term, such capabilities can be used to optimize the image acquisition to reduce the amount of dose or contrast media employed during the procedure. We present the automatic VOIDD algorithm to detect the vessel of intervention which is going to be treated during the procedure by combining information from the vessel image with contrast agent injection and images acquired during guidewire tip navigation. Due to the robust guidewire tip segmentation method, this algorithm is also able to automatically detect the sequence corresponding to guidewire navigation. We present an evaluation methodology which characterizes the correctness of the guide wire tip detection and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vessel-of-intervention with accuracy in the range of \(88\%\) or above and absence of tip with accuracy in range of \(98\%\) or above depending on the test case.

Keywords

Interventional cardiology PCI procedure modeling Image fusion Coronary roadmap 

References

  1. 1.
    Alt, H., Godau, M.: Measuring the resemblance of polygonal curves. In: Eighth Annual Symposium on Computational Geometry, pp. 102–109. ACM (1992)Google Scholar
  2. 2.
    Benseghir, T., Malandain, G., Vaillant, R.: A tree-topology preserving pairing for 3D/2D registration. IJCARS 10(6), 913–923 (2015)Google Scholar
  3. 3.
    Couprie, M., Bertrand, G.: Discrete topological transformations for image processing. In: Brimkov, V.E., Barneva, R.P. (eds.) Digital Geometry Algorithms. LNCVB, vol. 2, pp. 73–107. Springer, Dordrecht (2012). doi: 10.1007/978-94-007-4174-4_3 CrossRefGoogle Scholar
  4. 4.
    Hoffmann, M., Müller, S., Kurzidim, K., Strobel, N., Hornegger, J.: Robust identification of contrasted frames in fluoroscopic images. In: Handels, H., Deserno, T.M., Meinzer, H.-P., Tolxdorff, T. (eds.) Bildverarbeitung für die Medizin 2015. I, pp. 23–28. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-46224-9_6 Google Scholar
  5. 5.
    Honnorat, N., Vaillant, R., Paragios, N.: Graph-based guide-wire segmentation through fusion of contrast-enhanced and fluoroscopic images. In: ISBI 2012, pp. 948–951. IEEE (2012)Google Scholar
  6. 6.
    Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model-based detection of tubular structures in 3D images. CVIU 80(2), 130–171 (2000)zbMATHGoogle Scholar
  7. 7.
    Milletari, F., Belagiannis, V., Navab, N., Fallavollita, P.: Fully automatic catheter localization in C-arm images using \({\ell }1\)-sparse coding. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 570–577. Springer, Cham (2014). doi: 10.1007/978-3-319-10470-6_71 Google Scholar
  8. 8.
    Prasad, M., Cassar, A., Fetterly, K.A., Bell, M., Theessen, H., Ecabert, O., Bresnahan, J.F., Lerman, A.: Co-registration of angiography and intravascular ultrasound images through image-based device tracking. Cathet. Cardiovasc. Interv. 88(7), 1077–1082 (2015)CrossRefGoogle Scholar
  9. 9.
    Salembier, P., Wilkinson, M.H.: Connected operators. IEEE Signal Process. Mag. 26(6), 136–157 (2009)CrossRefGoogle Scholar
  10. 10.
    Xu, Y., Géraud, T., Najman, L.: Morphological filtering in shape spaces: applications using tree-based image representations. In: ICPR, pp. 485–488. IEEE (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.General Electric HealthcareBucFrance
  2. 2.Université Paris-Est, LIGM, A3SI, ESIEE ParisNoisy-le-GrandFrance

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