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

• URL: http://arxiv.org/abs/2404.08805v1
• Date: Fri, 12 Apr 2024 20:39:19 GMT
• Title: Real-time guidewire tracking and segmentation in intraoperative x-ray
• Authors: Baochang Zhang, Mai Bui, Cheng Wang, Felix Bourier, Heribert Schunkert, Nassir Navab,
• Abstract summary: We propose a two-stage deep learning framework for real-time guidewire segmentation and tracking. In the first stage, a Yolov5 detector is trained, using the original X-ray images as well as synthetic ones, to output the bounding boxes of possible target guidewires. In the second stage, a novel and efficient network is proposed to segment the guidewire in each detected bounding box.
• Score: 52.51797358201872
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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