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.

提出了一种用于实时导丝分割和跟踪的两阶段深度学习框架，包括一个用于精确定位导丝的 Yolov5s 检测器和一个用于分割导丝的新颖高效网络，通过定量和定性评估证明该方法在临床术中图像上表现出更高的鲁棒性和比当前技术更好的性能。