  We propose a novel deep-learning-based system for vessel segmentation.
Existing methods using CNNs have mostly relied on local appearances learned on
the regular image grid, without considering the graphical structure of vessel
shape. To address this, we incorporate a graph convolutional network into a
unified CNN architecture, where the final segmentation is inferred by combining
the different types of features. The proposed method can be applied to expand
any type of CNN-based vessel segmentation method to enhance the performance.
Experiments show that the proposed method outperforms the current
state-of-the-art methods on two retinal image datasets as well as a coronary
artery X-ray angiography dataset.
