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

During endovascular interventions, physicians have to perform accurate and\nimmediate operations based on the available real-time information, such as the\nshape and position of guidewires observed on the fluoroscopic images, haptic\ninformation and the patients' physiological signals. For this purpose,\nreal-time and accurate guidewire segmentation and tracking can enhance the\nvisualization of guidewires and provide visual feedback for physicians during\nthe intervention as well as for robot-assisted interventions. Nevertheless,\nthis task often comes with the challenge of elongated deformable structures\nthat present themselves with low contrast in the noisy fluoroscopic image\nsequences. To address these issues, a two-stage deep learning framework for\nreal-time guidewire segmentation and tracking is proposed. In the first stage,\na Yolov5s detector is trained, using the original X-ray images as well as\nsynthetic ones, which is employed to output the bounding boxes of possible\ntarget guidewires. More importantly, a refinement module based on\nspatiotemporal constraints is incorporated to robustly localize the guidewire\nand remove false detections. In the second stage, a novel and efficient network\nis proposed to segment the guidewire in each detected bounding box. The network\ncontains two major modules, namely a hessian-based enhancement embedding module\nand a dual self-attention module. Quantitative and qualitative evaluations on\nclinical intra-operative images demonstrate that the proposed approach\nsignificantly outperforms our baselines as well as the current state of the art\nand, in comparison, shows higher robustness to low quality images.\n