DeepBranchTracer: A Generally-Applicable Approach to Curvilinear
  Structure Reconstruction Using Multi-Feature Learning
Chao Liu Ting Zhao Nenggan Zheng
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.