用Transformer预测导丝在血管中的连续弯曲形状，实现可解释的自主导航。
SplineFormer: An Explainable Transformer-Based Approach for Autonomous Endovascular Navigation
- 基于Transformer构建分段样条表示，捕捉导丝复杂形变。
- 在真实机器人上实现自主导航，成功率达50%（主动脉弓分支动脉穿刺）。
- 输出可解释的路径信息，适合医疗机器人研发与临床应用。
血管内导航是微创手术的关键环节，精确控制如导丝等曲线器械对成功干预至关重要。其核心挑战在于实时准确预测导丝在血管中因与管壁相互作用而产生的复杂形变。传统分割方法难以在动态环境中提供可靠的实时形状预测。为此，我们提出SplineFormer，一种专为预测导丝连续平滑形状设计的可解释Transformer架构。通过将导丝建模为样条曲线，网络有效捕捉其弯曲与扭转特征，并以紧凑信息嵌入端到端机器人导航系统。实验表明，该方法可在真实机器人上实现自主血管内导航，在主动脉弓分支动脉穿刺任务中达到50%的成功率。
原文摘要 · Abstract (English)
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.
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