DOI: 10.1002/rcs.2569 ISSN:

A multiple catheter tips tracking method in X‐ray fluoroscopy images by a new lightweight segmentation network and Bayesian filtering

Hui Tang, Hao Kai Li, Chun Feng Yang, Jean‐Louis Dillenseger, Gouenou Coatrieux, Juan Feng, Shou Jun Zhou, Yang Chen
  • Computer Science Applications
  • Biophysics
  • Surgery


During percutaneous coronary intervention, the guiding catheter plays an important role. Tracking the catheter tip placed at the coronary ostium in the X‐ray fluoroscopy sequence can obtain image displacement information caused by the heart beating, which can help dynamic coronary roadmap overlap on X‐ray fluoroscopy images. Due to a low exposure dose, the X‐ray fluoroscopy is noisy and low contrast, which causes some difficulties in tracking. In this paper, we developed a new catheter tip tracking framework. First, a lightweight efficient catheter tip segmentation network is proposed and boosted by a self‐distillation training mechanism. Then, the Bayesian filtering post‐processing method is used to consider the sequence information to refine the single image segmentation results. By separating the segmentation results into several groups based on connectivity, our framework can track multiple catheter tips. The proposed tracking framework is validated on a clinical X‐ray sequence dataset.

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