title: Fully automatic catheter localization in C-arm images using ł1-sparse coding.
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.
authors.forename: Fausto
authors.initials: F
authors.lastname: Milletari
authors.forename: Vasileios
authors.initials: V
authors.lastname: Belagiannis
authors.forename: Nassir
authors.initials: N
authors.lastname: Navab
authors.forename: Pascal
authors.initials: P
authors.lastname: Fallavollita
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D006328:Cardiac Catheterization / Q000295:instrumentation / Q000379:methods*; D062906:Cardiac Catheters; D017115:Catheter Ablation / Q000295:instrumentation / Q000379:methods*; D005471:Fluoroscopy / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D025321:Surgery, Computer-Assisted / Q000379:methods*