Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach
Kurzendorfer T, Mewes P, Maier A, Strobel N, Brost A (2016)
Publication Type: Journal article
Publication year: 2016
Journal
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 35
Pages Range: 1892-1902
Journal Issue: 8
URI: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2016/Kurzendorfer16-CCL.pdf
Abstract
Cryo-balloon catheters have attracted an increasing amount of interest in the medical community as they can reduce patient risk during left atrial pulmonary vein ablation procedures. As cryo-balloon catheters are not equipped with electrodes, they cannot be localized automatically by electro-anatomical mapping systems. As a consequence, X-ray fluoroscopy has remained an important means for guidance during the procedure. Most recently, image guidance methods for fluoroscopy-based procedures have been proposed, but they provide only limited support for cryo-balloon catheters and require significant user interaction. To improve this situation, we propose a novel method for automatic cryo-balloon catheter detection in fluoroscopic images by detecting the cryo-balloon catheter's built-in X-ray marker. Our approach is based on a blob detection algorithm to find possible X-ray marker candidates. Several of these candidates are then excluded using prior knowledge. For the remaining candidates, several catheter specific features are introduced. They are processed using a machine learning approach to arrive at the final X-ray marker position. Our method was evaluated on 75 biplane fluoroscopy images from 40 patients, from two sites, acquired with a biplane angiography system. The method yielded a success rate of 99.0% in plane A and 90.6% in plane B, respectively. The detection achieved an accuracy of 1.00mm ± 0.82mm in plane A and 1.13mm ± 0.24mm in plane B. The localization in 3-D was associated with an average error of 0.36mm ± 0.86mm.
Authors with CRIS profile
Tanja Kurzendorfer
Lehrstuhl für Informatik 14 (Bild- und Sprachverarbeitung)
Andreas Maier
Lehrstuhl für Informatik 5 (Mustererkennung)
Involved external institutions
Germany (DE)How to cite
APA:
Kurzendorfer, T., Mewes, P., Maier, A., Strobel, N., & Brost, A. (2016). Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach. IEEE Transactions on Medical Imaging, 35(8), 1892-1902. https://dx.doi.org/10.1109/TMI.2016.2537052
MLA:
Kurzendorfer, Tanja, et al. "Cryo-Balloon Catheter Localization Based on a Support-Vector-Machine Approach." IEEE Transactions on Medical Imaging 35.8 (2016): 1892-1902.
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