Robust Landmark-based Stent Tracking in X-ray Fluoroscopy

TL;DR

This paper introduces a deep learning framework for accurate, fast, and robust landmark-based stent tracking in noisy X-ray fluoroscopy images, improving clinical device placement during angioplasty.

Contribution

It presents a novel end-to-end deep learning approach combining landmark detection, proposal, and GCN-based tracking for angioplasty devices.

Findings

Significantly better detection accuracy than existing models.

Fast inference speed suitable for clinical use.

Effective handling of noisy, complex angioplasty scenes.

Abstract

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.