Real-time guidewire tracking and segmentation in intraoperative x-ray
<|reference_start|>Real-time guidewire tracking and segmentation in intraoperative x-ray: During endovascular interventions, physicians have to perform accurate and immediate operations based on the available real-time information, such as the shape and position of guidewires observed on the fluoroscopic images, haptic information and the patients' physiological signals. For this purpose, real-time and accurate guidewire segmentation and tracking can enhance the visualization of guidewires and provide visual feedback for physicians during the intervention as well as for robot-assisted interventions. Nevertheless, this task often comes with the challenge of elongated deformable structures that present themselves with low contrast in the noisy fluoroscopic image sequences. To address these issues, a two-stage deep learning framework for real-time guidewire segmentation and tracking is proposed. In the first stage, a Yolov5s detector is trained, using the original X-ray images as well as synthetic ones, which is employed to output the bounding boxes of possible target guidewires. More importantly, a refinement module based on spatiotemporal constraints is incorporated to robustly localize the guidewire and remove false detections. In the second stage, a novel and efficient network is proposed to segment the guidewire in each detected bounding box. The network contains two major modules, namely a hessian-based enhancement embedding module and a dual self-attention module. Quantitative and qualitative evaluations on clinical intra-operative images demonstrate that the proposed approach significantly outperforms our baselines as well as the current state of the art and, in comparison, shows higher robustness to low quality images.<|reference_end|>
@article{zhang2024real-time,
    title={Real-time guidewire tracking and segmentation in intraoperative x-ray},
    author={Baochang Zhang, Mai Bui, Cheng Wang, Felix Bourier, Heribert
  Schunkert, Nassir Navab},
    journal={arXiv preprint arXiv:2404.08805},
    year={2024},
    doi={10.1117/12.2611097},
    archivePrefix={arXiv},
    eprint={2404.08805},
    primaryClass={eess.IV cs.CV cs.LG}
}