Fully automatic catheter localization in C-arm images using ℓ1-sparse coding
Milletari F, Navab N, Fallavollita P, Belagiannis V (2014)
Publication Type: Conference contribution
Publication year: 2014
Journal
Publisher: Springer Verlag
Book Volume: 8674 LNCS
Pages Range: 570-577
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: USA
ISBN: 9783319104690
DOI: 10.1007/978-3-319-10470-6_71
Abstract
We propose a method to perform automatic detection and tracking of electrophysiology (EP) catheters in C-arm fluoroscopy sequences. Our approach does not require any initialization, is completely automatic, and can concurrently track an arbitrary number of overlapping catheters. After a pre-processing step, we employ sparse coding to first detect candidate catheter tips, and subsequently detect and track the catheters. The proposed technique is validated on 2835 C-arm images, which include 39,690 manually selected ground-truth catheter electrodes. Results demonstrated sub-millimeter detection accuracy and real-time tracking performances. © 2014 Springer International Publishing.
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Germany (DE)How to cite
APA:
Milletari, F., Navab, N., Fallavollita, P., & Belagiannis, V. (2014). Fully automatic catheter localization in C-arm images using ℓ1-sparse coding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 570-577). USA: Springer Verlag.
MLA:
Milletari, Fausto, et al. "Fully automatic catheter localization in C-arm images using ℓ1-sparse coding." Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2014, USA Springer Verlag, 2014. 570-577.
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