标题： SplineFormer：一种可解释的基于Transformer的自主血管内导航方法 显示英文标题

标题： SplineFormer: An Explainable Transformer-Based Approach for Autonomous Endovascular Navigation

摘要： 血管内导航是微创手术的一个关键方面，其中对弯曲器械如导丝的精确控制对于成功干预至关重要。 这项任务中的一个关键挑战是准确预测导丝在血管中导航时不断变化的形状，由于与血管壁的相互作用，这会带来复杂的变形。 传统的分割方法往往无法提供准确的实时形状预测，限制了它们在高度动态环境中的有效性。 为了解决这个问题，我们提出了SplineFormer，一种新的基于变压器的架构，专门设计用于以可解释的方式预测导丝的连续平滑形状。 通过利用变压器的能力，我们的网络有效地捕捉了导丝的复杂弯曲和扭转，将其表示为样条以提高准确性和平滑性。 我们通过利用压缩信息将我们的SplineFormer集成到端到端机器人导航系统中。 实验结果表明，我们的SplineFormer能够自主进行血管内导航，并在真实机器人上插管头臂动脉时达到50%的成功率。 显示英文摘要

摘要： Endovascular navigation is a crucial aspect of minimally invasive procedures, where precise control of curvilinear instruments like guidewires is critical for successful interventions. A key challenge in this task is accurately predicting the evolving shape of the guidewire as it navigates through the vasculature, which presents complex deformations due to interactions with the vessel walls. Traditional segmentation methods often fail to provide accurate real-time shape predictions, limiting their effectiveness in highly dynamic environments. To address this, we propose SplineFormer, a new transformer-based architecture, designed specifically to predict the continuous, smooth shape of the guidewire in an explainable way. By leveraging the transformer's ability, our network effectively captures the intricate bending and twisting of the guidewire, representing it as a spline for greater accuracy and smoothness. We integrate our SplineFormer into an end-to-end robot navigation system by leveraging the condensed information. The experimental results demonstrate that our SplineFormer is able to perform endovascular navigation autonomously and achieves a 50% success rate when cannulating the brachiocephalic artery on the real robot.