中国科学院自动化研究所机构知识库

In endovascular and cardiovascular surgery, realtime guidewire morphological and positional analysis is an important pre-requisite for robot-assisted intervention, which can aid in reducing radiation dose, contrast agent and procedure time. Nevertheless, this task often comes with the challenge of the deformable elongated structure with low contrast in noisy X-ray fluoroscopy. In this paper, a real-time multi-functional framework is proposed for fully automatic guidewire morphological and positional analysis, namely guidewire segmentation, endpoint localization and angle measurement. In the first stage, the proposed Fast Attention Recurrent Network (FAR-Net) achieves real-time and accurate guidewire segmentation. In the second stage, the endpoint localization and angle measurement algorithm robustly obtain sub-pixel-level endpoint and angle of the guidewire tip. Quantitative and qualitative evaluations on MSGSeg dataset consisting of 180 X-ray sequences from 30 patients demonstrate that the proposed framework significantly outperforms simpler baselines as well as the best previously-published result for this task. The proposed approach reached F1-Score of 0.938, mean distance error of 0.596 pixels, endpoint localization & angle measurement accuracy of 97.8% & 95.3%, and inference rate of approximately 13 FPS. The proposed framework not only addresses the issues of extreme class imbalance and misclassified examples, but also meets the real-time requirements, achieving the state-of-the-art performance. The proposed approach is promising for integration into robotic navigation frameworks to various intravascular applications, enabling robotic-assisted intervention.