Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

D. Volpi, M. H. Sarhan, R. Ghotbi, N. Navab, D. Mateus, S. Demirci
Online Tracking of Interventional Devices for Endovascular Aortic Repair
International Journal of Computer Assisted Radiology and Surgery, June 2015, Volume 10, Issue 6, pp 773-781 (bib)

The continuous integration of innovative imaging modalities into conventional vascular surgery rooms has led to an urgent need for computer assistance solutions that support the smooth integration of imaging within the surgical workflow. In particular endovascular interventions performed under 2D Fluoroscopic or Angiographic imaging only, require reliable and fast navigation support for complex treatment procedures such as endovascular aortic repair (EVAR). Despite the vast variety of image-based guide wire and catheter tracking methods, an adoption of these for detecting and tracking the stent graft delivery device is not possible due to its special geometry and intensity appearance. In this paper, we present, for the first time, the automatic detection and tracking of the stent graft delivery device in 2D Fluoroscopic sequences on the fly. The proposed approach is based on the Robust Principal Component Analysis (RPCA) and extends the conventional batch processing towards an online tracking system that is able to detect and track medical devices on the fly. The proposed method has been tested on interventional sequences of 4 different clinical cases. In the lack of publicly available ground truth data, we have further initiated a crowd sourcing strategy that has resulted in 200 annotations by unexperienced users, 120 of which were used to establish a ground truth dataset for quantitatively evaluating our algorithm. In addition, we have performed a user study amongst our clinical partners for qualitative evaluation of the results. Although we calculated an average error in the range of 9 pixels, the fact that our tracking method functions on the fly and is able to detect stent grafts in all unfolding stages without fine-tuning of parameters has convinced our clinical partners and they all agreed on the very high clinical relevance of our method.
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