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

Baochang Zhang; Mai Bui; Cheng Wang; Felix Bourier; Heribert Schunkert; Nassir Navab

doi:10.1117/12.2611097

4 April 2022 Real-time guidewire tracking and segmentation in intraoperative x-ray

Baochang Zhang, Mai Bui, Cheng Wang, Felix Bourier, Heribert Schunkert, Nassir Navab

Proceedings Volume 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling; 120341A (2022) https://doi.org/10.1117/12.2611097
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

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.

Conference Presentation

Citation Download Citation

Baochang Zhang, Mai Bui, Cheng Wang, Felix Bourier, Heribert Schunkert, and Nassir Navab "Real-time guidewire tracking and segmentation in intraoperative x-ray", Proc. SPIE 12034, Medical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling, 120341A (4 April 2022); https://doi.org/10.1117/12.2611097

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