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Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Fusion of preoperative data with intraoperative X-ray images has proven the potential to reduce radiation exposure and contrast agent, especially for complex endovascular aortic repair (EVAR). Due to patient movement and introduced devices that deform the vasculature, the fusion can become inaccurate. This is usually detected by comparing the preoperative information with the contrasted vessel. To avoid repeated use of iodine, comparison with an implanted stent can be used to adjust the fusion. However, detecting the stent automatically without the use of contrast is challenging as only thin stent wires are visible.

Method

We propose a fast, learning-based method to segment aortic stents in single uncontrasted X-ray images. To this end, we employ a fully convolutional network with residual units. Additionally, we investigate whether incorporation of prior knowledge improves the segmentation.

Results

We use 36 X-ray images acquired during EVAR for training and evaluate the segmentation on 27 additional images. We achieve a Dice coefficient of 0.933 (AUC 0.996) when using X-ray alone, and 0.918 (AUC 0.993) and 0.888 (AUC 0.99) when adding the preoperative model, and information about the expected wire width, respectively.

Conclusion

The proposed method is fully automatic, fast and segments aortic stent grafts in fluoroscopic images with high accuracy. The quality and performance of the segmentation will allow for an intraoperative comparison with the preoperative information to assess the accuracy of the fusion.

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Authors and Affiliations

  1. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    Katharina Breininger, Tanja Kurzendorfer & Andreas Maier

  2. Computer Aided Medical Procedures (CAMP), Technische Universität München, Munich, Germany

    Shadi Albarqouni

  3. Siemens Healthcare GmbH, Forchheim, Germany

    Tanja Kurzendorfer, Marcus Pfister & Markus Kowarschik

  4. School of Advanced Optical Techniques (SAOT), Erlangen, Germany

    Andreas Maier

Authors
  1. Katharina Breininger
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  2. Shadi Albarqouni
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  3. Tanja Kurzendorfer
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  4. Marcus Pfister
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  5. Markus Kowarschik
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  6. Andreas Maier
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Corresponding author

Correspondence to Katharina Breininger.

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Conflict of interest

S. Albarqouni and A. Maier have no conflict of interest to declare. K. Breininger is funded by Siemens Healthcare GmbH. T. Kurzendorfer, M. Pfister, and M. Kowarschik are employees of Siemens Healthcare GmbH.

Ethical approval

This study has been performed retrospectively. For this type of study formal consent is not required.

Informed consent

Informed consent was obtained from all individual participants included in the original study.

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Disclaimer: The methods and information presented in this work are based on research and are not commercially available.

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Breininger, K., Albarqouni, S., Kurzendorfer, T. et al. Intraoperative stent segmentation in X-ray fluoroscopy for endovascular aortic repair. Int J CARS 13, 1221–1231 (2018). https://doi.org/10.1007/s11548-018-1779-6

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  • DOI: https://doi.org/10.1007/s11548-018-1779-6

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