🤖 AI Summary
This work addresses the challenge of topological errors—such as spurious connections and centerline fractures—in automatic extraction of 3D tubular structure skeletons, which commonly arise from noise or missing data and are inefficient and error-prone to correct manually. The authors propose a lightweight semi-automatic correction method: given user-specified start and end points, it performs locally stable propagation via component-wise minimum spanning trees and bridges gaps or resolves ambiguous connections using filtered 3D Delaunay edge graphs. Candidate paths are ranked through a scoring mechanism that integrates directional continuity, spatial proximity, component consistency, and goal-directedness. Implemented in C++ with Libigl, the interactive system effectively repairs typical artifacts like “crossings” and “breaks” in cerebral vasculature data, producing ordered polylines suitable for downstream processing and demonstrating both practical utility and robustness.
📝 Abstract
Skeletonization of tubular structures from 3D imaging is essential for tasks such as morphometric analysis, transport or flow simulation, and procedural planning in domains including vascular networks, plant root systems, and neural connectomes. However, automatic skeleton extraction often introduces topological artifacts, such as erroneous connections between nearby branches and fragmented centerlines caused by noise or missing data. Correcting these artifacts manually can be time-consuming and error-prone, especially when precise interaction is required.
We present a semi-automatic correction method that reconstructs a plausible centerline connection from minimal user input. Given a user-selected source and target point, our method traces a path by combining (i) component-wise minimum spanning trees for stable local propagation and (ii) a filtered 3D Delaunay edge graph for bridging gaps and handling ambiguous junctions. Candidate steps are ranked using a score that accounts for direction continuity, spatial proximity, component consistency, and target-directed progress. The output is an ordered polyline (or edge sequence) that can be used as a suggested correction and integrated into downstream skeleton post-processing workflows.
We implement the system in C++ with an interactive viewer based on Libigl and demonstrate representative qualitative results on brain vessel datasets, including correction of typical "crossing" and "dotted" artifacts. While our current validation is qualitative, the method is lightweight and serves as a practical building block toward more comprehensive interactive correction pipelines in biomedical imaging and related domains.