Paper Info
Title
Catheter tracking via online learning for dynamic motion compensation in transcatheter aortic valve implantation.
Abstract
Dynamic overlay of 3D models onto 2D X-ray images has important applications in image guided interventions. In this paper, we present a novel catheter tracking for motion compensation in the Transcatheter Aortic Valve Implantation (TAVI). To address such challenges as catheter shape and appearance changes, occlusions, and distractions from cluttered backgrounds, we present an adaptive linear discriminant learning method to build a measurement model online to distinguish catheters from background. An analytic solution is developed to effectively and efficiently update the discriminant model and to minimize the classification errors between the tracking object and backgrounds. The online learned discriminant model is further combined with an offline learned detector and robust template matching in a Bayesian tracking framework. Quantitative evaluations demonstrate the advantages of this method over current state-of-the-art tracking methods in tracking catheters for clinical applications.
Year
DOI
Venue
2012
10.1007/978-3-642-33418-4_3
MICCAI (2)
Keywords
Field
DocType
discriminant model,x-ray image,tracking object,adaptive linear discriminant,transcatheter aortic valve implantation,dynamic motion compensation,bayesian tracking framework,online learning,catheter shape,measurement model online,novel catheter tracking,current state-of-the-art tracking method
Template matching,Computer vision,Catheter,Pattern recognition,Computer science,Motion compensation,Aortic valve,Artificial intelligence,Linear discriminant analysis,Overlay,Detector,Bayesian probability
Conference
Volume
Issue
ISSN
15
Pt 2
0302-9743
Citations 
PageRank 
References 
2
0.42
10
Authors
4
Name
Order
Citations
PageRank
Peng Wang1778.30
Yefeng Zheng21391114.67
Matthias John311911.36
Dorin Comaniciu48389601.83