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.

本论文提出了一种针对单支支架的端到端深度学习框架，用于股动脉成形术中 X 射线荧光镜下的设备跟踪，该方法包含三个模块，即基于 U-Net 的地标检测、基于 ResNet 的支架提取和特征提取，以及基于图卷积神经网络的支架跟踪，实验结果表明与最先进的基于点追踪模型相比，我们的方法在检测方面表现明显更好。此外，其快速推断速度满足临床需求。