Device tracking is an important prerequisite for guidance during endovascular
procedures. Especially during cardiac interventions, detection and tracking of
guiding the catheter tip in 2D fluoroscopic images is important for
applications such as mapping vessels from angiography (high dose with contrast)
to fluoroscopy (low dose without contrast). Tracking the catheter tip poses
different challenges: the tip can be occluded by contrast during angiography or
interventional devices; and it is always in continuous movement due to the
cardiac and respiratory motions. To overcome these challenges, we propose
ConTrack, a transformer-based network that uses both spatial and temporal
contextual information for accurate device detection and tracking in both X-ray
fluoroscopy and angiography. The spatial information comes from the template
frames and the segmentation module: the template frames define the surroundings
of the device, whereas the segmentation module detects the entire device to
bring more context for the tip prediction. Using multiple templates makes the
model more robust to the change in appearance of the device when it is occluded
by the contrast agent. The flow information computed on the segmented catheter
mask between the current and the previous frame helps in further refining the
prediction by compensating for the respiratory and cardiac motions. The
experiments show that our method achieves 45% or higher accuracy in detection
and tracking when compared to state-of-the-art tracking models.

提出了一种基于转换器的网络 ConTrack，利用空间和时间上下文信息在 X 光荧光和描记术图像中进行准确的设备检测和跟踪。

ConTrack：X-ray 设备追踪的上下文变换器

ConTrack: Contextual Transformer for Device Tracking in X-ray

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。