Real-time guidewire tracking and segmentation in intraoperative x-ray
Baochang Zhang Mai Bui Cheng Wang Felix Bourier Heribert Schunkert Nassir Navab
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.