A Framework of Wire Tracking in Image Guided Interventions

Abstract

This chapter presents a framework of using computer vision and machine learning methods to tracking guidewire, a medical device inserted into vessels dur- ing image guided interventions. During interventions, the guidewire exhibits non- rigid deformation due to patients’ breathing and cardiac motions. Such 3D motions are complicated when being projected onto the 2D fluoroscopy. Furthermore, flu- oroscopic images have severe image artifacts and other wire-like structures. Those factors make robust guidewire tracking a challenging problem. To address these challenges, this chapter presents a probabilistic framework for the purpose of ro- bust tracking. We introduce a semantic guidewire model that contains three parts, including a catheter tip, a guidewire tip and a guidewire body.Measurements of dif- ferent parts are integrated into a Bayesian framework as measurements of a whole guidewire for robust guidewire tracking. For each part, two types of measurements, one from learning-based detectors and the other from appearance models, are com- bined. A hierarchical and multi-resolution tracking scheme based on kernel-based measurement smoothing is then developed to track guidewires effectively and effi- ciently in a coarse-to-fine manner. The framework has been validated on a testing set containing 47 sequences acquired under clinical environments, and achieves a mean tracking error of less than 2 pixels.