Robust Landmark-based Stent Tracking in X-ray Fluoroscopy
This addresses the challenge of accurate stent placement for clinicians in angioplasty procedures, though it is incremental as it builds on existing deep learning methods for a specific medical imaging task.
The paper tackles the problem of tracking stents with radiopaque markers in noisy X-ray fluoroscopy images during angioplasty, proposing an end-to-end deep learning framework that significantly outperforms state-of-the-art point-based tracking models in detection and meets clinical speed requirements.
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.