  Vessel segmentation is widely used to help with vascular disease diagnosis.
Vessels reconstructed using existing methods are often not sufficiently
accurate to meet clinical use standards. This is because 3D vessel structures
are highly complicated and exhibit unique characteristics, including sparsity
and anisotropy. In this paper, we propose a novel hybrid deep neural network
for vessel segmentation. Our network consists of two cascaded subnetworks
performing initial and refined segmentation respectively. The second subnetwork
further has two tightly coupled components, a traditional CNN-based U-Net and a
graph U-Net. Cross-network multi-scale feature fusion is performed between
these two U-shaped networks to effectively support high-quality vessel
segmentation. The entire cascaded network can be trained from end to end. The
graph in the second subnetwork is constructed according to a vessel probability
map as well as appearance and semantic similarities in the original CT volume.
To tackle the challenges caused by the sparsity and anisotropy of vessels, a
higher percentage of graph nodes are distributed in areas that potentially
contain vessels while a higher percentage of edges follow the orientation of
potential nearbyvessels. Extensive experiments demonstrate our deep network
achieves state-of-the-art 3D vessel segmentation performance on multiple public
and in-house datasets.
