Optimal curve methods provide a fundamental framework for tubular centerline tracking. Point-wise approaches, such as minimal paths, are theoretically elegant but often suffer from shortcut and short-branch combination problems in complex scenarios. Nonlocal segment-wise methods address these issues by mapping pre-extracted centerline fragments onto a segment-proposal graph, performing optimization in this abstract space, and recovering the target tubular centerline from the resulting optimal path. In this paradigm, graph construction is critical, as it directly determines the quality of the final result. However, existing segment-wise methods construct graphs in a static manner, requiring all edges and their weights to be pre-computed, i.e. the graph must be sufficiently complete prior to search. Otherwise, the true path may be absent from the candidate space, leading to search failure. To address this limitation, we propose a dynamic exploration scheme for constructing segment-proposal graphs, where the graph is built on demand during the search for optimal paths. By formulating the problem as a Markov decision process, we apply Q-learning to compute edge weights only for visited transitions and adaptively expand the action space when connectivity is insufficient. Experimental results on retinal vessels, roads, and rivers demonstrate consistent improvements over state-of-the-art methods in both accuracy and efficiency.

翻译：最优曲线方法为管状中心线追踪提供了基础框架。逐点方法（如最小路径）在理论上具有简洁性，但在复杂场景中常受限于捷径与短分支合并问题。非局部的逐段方法通过将预提取的中心线片段映射到片段提议图上，在此抽象空间中进行优化，并从所得最优路径中恢复目标管状中心线，从而解决了上述问题。在此范式中，图构建至关重要，因其直接决定了最终结果的质量。然而，现有逐段方法以静态方式构建图，要求所有边及其权重均需预先计算，即在搜索前图必须足够完整。否则，真实路径可能不存在于候选空间中，导致搜索失败。为克服这一局限，我们提出一种用于构建片段提议图的动态探索方案，该方案在最优路径搜索过程中按需构建图。通过将问题建模为马尔可夫决策过程，我们应用Q学习仅对已访问的转移计算边权重，并在连通性不足时自适应扩展动作空间。在视网膜血管、道路及河流数据集上的实验结果表明，该方法在准确性与效率上均较现有先进方法取得了一致性提升。