Deep Vessel Segmentation By Learning Graphical Connectivity

Understand

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.