Fully automatic catheter localization in C-arm images using ł1-sparse coding

Fausto Milletari, Vasileios Belagiannis, Nassir Navab, Pascal Fallavollita

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

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