Eigenvector Grouping for Point Cloud Vessel Labeling

Patryk Tadeusz Rygiel, Maciej Zieba, Tomasz Konopczynski

Keywords: point clouds, vector grouping, medical image segmentation, coronary artery segmentation

Abstract:

Segmentation of coronary arteries from Coronary Computed Tomography Angiography
(CCTA) is an essential step in developing various noninvasive diagnostic methods. In this
work, we tackle the task of vessel labeling on coronary artery voxel-based prediction by use
of point cloud artificial neural network. We propose a novel point aggregation technique
Eigenvector Grouping (EVG), tailored to the analysis of tubular-like structures. We further
utilize a specifically designed post-processing technique Component-Wise Majority Point
Voting (CMPV), to refine point cloud segmentation by enforcing class consistency among
connected components. We show that our solution outperforms previously proposed
methods in the vessel labeling task on a CCTA dataset especially, in the presence of
disrupted segmentations.