Curvilinear structures, which include line-like continuous objects, are fundamental geometrical elements in image-based applications. Reconstructing these structures from images constitutes a pivotal research area in computer vision. However, the complex topology and ambiguous image evidence render this process a challenging task. In this paper, we introduce DeepBranchTracer, a novel method that learns both external image features and internal geometric characteristics to reconstruct curvilinear structures. Firstly, we formulate the curvilinear structures extraction as a geometric attribute estimation problem. Then, a curvilinear structure feature learning network is designed to extract essential branch attributes, including the image features of centerline and boundary, and the geometric features of direction and radius. Finally, utilizing a multi-feature fusion tracing strategy, our model iteratively traces the entire branch by integrating the extracted image and geometric features. We extensively evaluated our model on both 2D and 3D datasets, demonstrating its superior performance over existing segmentation and reconstruction methods in terms of accuracy and continuity.

翻译：曲线结构（包含线状连续目标）是图像应用中的基本几何元素。从图像中重建这些结构构成了计算机视觉领域的关键研究方向。然而，复杂的拓扑结构与模糊的图像证据使这一过程充满挑战。本文提出DeepBranchTracer——一种同时学习外部图像特征与内部几何特性的曲线结构重建新方法。首先，我们将曲线结构提取建模为几何属性估计问题；随后设计曲线结构特征学习网络，用于提取分支的核心属性，包括中心线与边界的图像特征，以及方向与半径的几何特征；最后，通过多特征融合追踪策略，模型整合提取的图像与几何特征，迭代完成整个分支的追踪。我们在二维与三维数据集上进行了全面评估，证明该方法在精度与连续性方面均显著优于现有分割与重建方法。