Deep Vessel Segmentation By Learning Graphical Connectivity

TL;DR

This paper introduces a deep learning system that integrates graph convolutional networks with CNNs to improve vessel segmentation by capturing the graphical structure of vessels, outperforming existing methods.

Contribution

It presents a novel unified CNN architecture incorporating graph connectivity for vessel segmentation, enhancing performance over state-of-the-art techniques.

Findings

Outperforms current state-of-the-art on retinal datasets

Effective in coronary artery X-ray angiography

Enhances vessel segmentation accuracy

Abstract

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.