Tubular tree structures such as blood vessels and lung airways are central to many clinical tasks, including diagnosis, treatment planning, and surgical navigation. Accurate centerline extraction with correct topology is essential, as missing small branches can lead to incomplete assessments or overlooked abnormalities. We propose RefTr, a 3D image-to-graph framework that generates vascular centerlines via recurrent refinement of confluent trajectories. RefTr adopts a Transformer-based Producer-Refiner architecture in which the Producer predicts candidate trajectories and a shared Refiner iteratively refines them toward the target branches. The confluent trajectory representation enables whole-branch refinement while explicitly enforcing valid topology. This recurrent scheme improves precision and reduces decoder parameters by 2.4x compared to the state-of-the-art. We further introduce an efficient non-maximum suppression algorithm for spatial tree graphs to merge duplicate branches and extend evaluation metrics to be radius-aware for robust comparison. Experiments on multiple public datasets demonstrate stronger overall performance, faster inference, and substantially fewer parameters, highlighting the effectiveness of RefTr for 3D vascular tree analysis.

翻译：血管与肺气道等管状树结构是众多临床任务（如诊断、治疗规划与手术导航）的核心。具备正确拓扑结构的精确中心线提取至关重要，因为细小分支的缺失可能导致评估不完整或异常状况被忽视。本文提出RefTr，一种通过汇合轨迹的循环优化生成血管中心线的三维图像-图框架。RefTr采用基于Transformer的生产器-优化器架构：生产器预测候选轨迹，共享的优化器则通过迭代优化使其逼近目标分支。汇合轨迹表示法在实现整支分支优化的同时，显式保证了有效拓扑结构。该循环机制相比现有最优方法，在提升精度的同时将解码器参数量减少2.4倍。我们进一步提出面向空间树图的高效非极大值抑制算法以合并重复分支，并将评估指标扩展为半径感知型以实现鲁棒比较。在多个公开数据集上的实验表明，本方法具有更强的综合性能、更快的推理速度及显著更少的参数量，凸显了RefTr在三维血管树分析中的有效性。