  Automatic blood vessel extraction from 3D medical images is crucial for
vascular disease diagnoses. Existing methods based on convolutional neural
networks (CNNs) may suffer from discontinuities of extracted vessels when
segmenting such thin tubular structures from 3D images. We argue that
preserving the continuity of extracted vessels requires to take into account
the global geometry. However, 3D convolutions are computationally inefficient,
which prohibits the 3D CNNs from sufficiently large receptive fields to capture
the global cues in the entire image. In this work, we propose a hybrid
representation learning approach to address this challenge. The main idea is to
use CNNs to learn local appearances of vessels in image crops while using
another point-cloud network to learn the global geometry of vessels in the
entire image. In inference, the proposed approach extracts local segments of
vessels using CNNs, classifies each segment based on global geometry using the
point-cloud network, and finally connects all the segments that belong to the
same vessel using the shortest-path algorithm. This combination results in an
efficient, fully-automatic and template-free approach to centerline extraction
from 3D images. We validate the proposed approach on CTA datasets and
demonstrate its superior performance compared to both traditional and CNN-based
baselines.
