  In clinical procedures of angioplasty (i.e., open clogged coronary arteries),
devices such as balloons and stents need to be placed and expanded in arteries
under the guidance of X-ray fluoroscopy. Due to the limitation of X-ray dose,
the resulting images are often noisy. To check the correct placement of these
devices, typically multiple motion-compensated frames are averaged to enhance
the view. Therefore, device tracking is a necessary procedure for this purpose.
Even though angioplasty devices are designed to have radiopaque markers for the
ease of tracking, current methods struggle to deliver satisfactory results due
to the small marker size and complex scenes in angioplasty. In this paper, we
propose an end-to-end deep learning framework for single stent tracking, which
consists of three hierarchical modules: U-Net based landmark detection, ResNet
based stent proposal and feature extraction, and graph convolutional neural
network (GCN) based stent tracking that temporally aggregates both spatial
information and appearance features. The experiments show that our method
performs significantly better in detection compared with the state-of-the-art
point-based tracking models. In addition, its fast inference speed satisfies
clinical requirements.
