用双注意力网络实现实时导丝尖端追踪，提升血管介入手术精度。
Real-Time Guidewire Tip Tracking Using a Siamese Network for Image-Guided Endovascular Procedures
- 基于孪生网络与双注意力机制，融合自注意力与交叉注意力增强特征学习。
- 平均定位误差0.421±0.138 mm，IoU达0.782，处理速度57.2帧/秒。
- 适用于临床实时导航与机器人辅助介入，适合医疗AI与影像算法研究者。
人工智能在临床实践中的应用日益广泛，显著提升了医疗服务的效率与效果。本文聚焦心血管疾病图像引导治疗中的导丝尖端追踪任务，旨在帮助医生提高诊断与治疗质量。提出一种基于孪生网络的新型追踪框架，引入自注意力与交叉注意力双重机制，通过增强时空特征学习，有效应对视觉模糊、组织形变及成像伪影等挑战。在包含15个序列的临床数字减影血管造影（DSA）数据集中随机选取3个序列进行验证，结果显示平均定位误差为0.421±0.138毫米，最大误差1.736毫米，平均交并比（IoU）为0.782。系统平均处理速度达57.2帧/秒，满足血管介入成像的实时性要求。进一步在机器人平台上的自动化诊疗测试中，两种实验场景下的追踪误差分别为0.708±0.695毫米和0.148±0.057毫米。
原文摘要 · Abstract (English)
An ever-growing incorporation of AI solutions into clinical practices enhances the efficiency and effectiveness of healthcare services. This paper focuses on guidewire tip tracking tasks during image-guided therapy for cardiovascular diseases, aiding physicians in improving diagnostic and therapeutic quality. A novel tracking framework based on a Siamese network with dual attention mechanisms combines self- and cross-attention strategies for robust guidewire tip tracking. This design handles visual ambiguities, tissue deformations, and imaging artifacts through enhanced spatial-temporal feature learning. Validation occurred on 3 randomly selected clinical digital subtraction angiography (DSA) sequences from a dataset of 15 sequences, covering multiple interventional scenarios. The results indicate a mean localization error of 0.421 $\pm$ 0.138 mm, with a maximum error of 1.736 mm, and a mean Intersection over Union (IoU) of 0.782. The framework maintains an average processing speed of 57.2 frames per second, meeting the temporal demands of endovascular imaging. Further validations with robotic platforms for automating diagnostics and therapies in clinical routines yielded tracking errors of 0.708 $\pm$ 0.695 mm and 0.148 $\pm$ 0.057 mm in two distinct experimental scenarios.
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