Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images

2026-05-01

AI Summary

Existing methods for vessel segmentation and tracking struggle to reconstruct topologically accurate, complete three-dimensional vascular networks. This work proposes a novel approach that jointly predicts voxel-wise vessel orientation vectors and segmentation masks, and introduces an orientation-guided TEASAR algorithm to extract high-fidelity vascular graph structures. The method substantially improves the separation of closely adjacent vessels and enhances performance in handling multiple vascular trees, while also introducing an interpretable topological error metric—quantifying false splits and false merges. It achieves state-of-the-art results across three benchmarks encompassing both synthetic and real data, and has been successfully applied to micro-CT imaging of rat heart vasculature.

Abstract

Blood vessel segmentation and -tracing are essential tasks in many medical imaging applications. Although numerous methods exist, the prevailing segment-then-fix paradigm is fundamentally limited regarding its suitability for modeling the task of complete and topologically accurate vascular network reconstruction. Here, we propose an approach to extract topologically more accurate vascular graphs from 3D image data, building upon highly successful ideas from the related biomedical tasks of cell segmentation and -tracking. Our approach first predicts voxel-wise vessel direction vectors joint with standard vessel segmentation masks. Second, to extract the vascular graph from these predictions, we introduce a direction-vector-guided extension of the TEASAR algorithm. Our approach achieves state-of-the-art performance on three benchmark datasets, spanning both synthetic and real imagery. We further demonstrate the applicability of our approach to challenging 3D micro-CT scans of rat heart vasculature. Finally, we propose meaningful and interpretable measures of topological error, namely false splits and false merges for graphs. Overall, our approach substantially improves the topological accuracy of reconstructed vascular graphs, being able to separate closely apposed vessel segments and handle multiple vascular trees within a single volume.

Problem

• vascular graph reconstruction
• topological accuracy
• 3D vascular images
• vessel segmentation
• direction vectors

Innovation

• vessel graph reconstruction
• voxel-wise direction vectors
• topological accuracy
• TEASAR algorithm
• 3D vascular imaging