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。

实时在术中 X 射线中的导丝追踪和分割

Real-time guidewire tracking and segmentation in intraoperative x-ray

This study tackles key obstacles in adopting surgical navigation in
orthopedic surgeries, including time, cost, radiation, and workflow integration
challenges. Recently, our work X23D showed an approach for generating 3D
anatomical models of the spine from only a few intraoperative fluoroscopic
images. This negates the need for conventional registration-based surgical
navigation by creating a direct intraoperative 3D reconstruction of the
anatomy. Despite these strides, the practical application of X23D has been
limited by a domain gap between synthetic training data and real intraoperative
images.
In response, we devised a novel data collection protocol for a paired dataset
consisting of synthetic and real fluoroscopic images from the same
perspectives. Utilizing this dataset, we refined our deep learning model via
transfer learning, effectively bridging the domain gap between synthetic and
real X-ray data. A novel style transfer mechanism also allows us to convert
real X-rays to mirror the synthetic domain, enabling our in-silico-trained X23D
model to achieve high accuracy in real-world settings.
Our results demonstrated that the refined model can rapidly generate accurate
3D reconstructions of the entire lumbar spine from as few as three
intraoperative fluoroscopic shots. It achieved an 84% F1 score, matching the
accuracy of our previous synthetic data-based research. Additionally, with a
computational time of only 81.1 ms, our approach provides real-time
capabilities essential for surgery integration.
Through examining ideal imaging setups and view angle dependencies, we've
further confirmed our system's practicality and dependability in clinical
settings. Our research marks a significant step forward in intraoperative 3D
reconstruction, offering enhancements to surgical planning, navigation, and
robotics.

我们的研究通过解决手术导航中的时间、成本、辐射和工作流整合等关键障碍，提出了一个从少量术中透视 X 光图像生成脊柱三维解剖模型的方法，并通过收集体现真实和虚拟透视 X 光图像的配对数据集，利用迁移学习和风格转移机制提高了深度学习模型在真实术中环境中的准确性，研究成果表明我们的方法在手术规划、导航和机器人技术领域有重要的应用潜力。

基于真实荧光透视数据的腰椎三维重建领域适应策略

Domain adaptation strategies for 3D reconstruction of the lumbar spine  using real fluoroscopy data

Endovascular navigation, essential for diagnosing and treating endovascular
diseases, predominantly hinges on fluoroscopic images due to the constraints in
sensory feedback. Current shape reconstruction techniques for endovascular
intervention often rely on either a priori information or specialized
equipment, potentially subjecting patients to heightened radiation exposure.
While deep learning holds potential, it typically demands extensive data. In
this paper, we propose a new method to reconstruct the 3D guidewire by
utilizing CathSim, a state-of-the-art endovascular simulator, and a 3D
Fluoroscopy Guidewire Reconstruction Network (3D-FGRN). Our 3D-FGRN delivers
results on par with conventional triangulation from simulated monoplane
fluoroscopic images. Our experiments accentuate the efficiency of the proposed
network, demonstrating it as a promising alternative to traditional methods.

我们提出了一种利用 CathSim 和 3D-Fluoroscopy Guidewire Reconstruction Network (3D-FGRN) 重建三维导线的新方法，该方法能够与传统的三角化方法在模拟单平面荧光成像中达到相同水平，有效地证明了该网络作为传统方法的有希望的替代品。