Abstract
This chapter presents a framework of using computer vision and machine learning methods to tracking guidewire, a medical device inserted into vessels during image guided interventions. During interventions, the guidewire exhibits nonrigid deformation due to patients’ breathing and cardiac motions. Such 3D motions are complicated when being projected onto the 2D fluoroscopy. Furthermore, fluoroscopic 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 robust tracking. We introduce a semantic guidewire model that contains three parts, including a catheter tip, a guidewire tip and a guidewire body. Measurements of different 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 combined. A hierarchical and multi-resolution tracking scheme based on kernel-based measurement smoothing is then developed to track guidewires effectively and efficiently 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.
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Wang, P., Meyer, A., Chen, T., Zhou, S.K., Comaniciu, D. (2011). A Framework of Wire Tracking in Image Guided Interventions. In: Wang, L., Zhao, G., Cheng, L., Pietikäinen, M. (eds) Machine Learning for Vision-Based Motion Analysis. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-0-85729-057-1_7
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DOI: https://doi.org/10.1007/978-0-85729-057-1_7
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