Automatic coronary artery lumen segmentation in computed tomography angiography using paired multi-scale 3D CNN

Abstract

Coronary artery disease (CAD) is one of the leading causes of death worldwide. The computed tomography angiography (CTA) is increasingly used to diagnose CAD due to its non-invasive nature and high-resolution three-dimensional (3D) imaging capability of the coronary artery anatomy. CTA allows for identification and grading of stenosis by evaluating the degree of narrowing of the blood-filled coronary artery lumen. Both identification and grading rely on the precise segmentation of the coronary arteries on CTA images. In this paper, a fully automatic segmentation framework is proposed to extract the coronary arteries from the whole cardiac CTA images. The framework adopts a paired multi-scale 3D deep convolutional neural networks (CNNs) to identify which voxels belong to the vessel lumen. Voxels that may belong to coronary artery lumen are recognized by the first CNN in the pair and both artery positives and artery-like negatives are distinguished by the second one. Each CNN is assigned to a different task. They share the same architecture in common but with different weights. In order to combine local and larger contextual information, we adopt a dual pathway architecture that can process the input image simultaneously on multiple scales. The experiments were performed on a CTA dataset from 44 patients. 35 CTA scans are used for training and the rests for testing. The proposed segmentation framework achieved a mean Dice similarity coefficient (DSC) of 0.8649 and mean surface distance (MSD) of 0.5571 with reference to manual annotations. Experimental results show that the proposed framework is capable of performing complete, accurate and robust segmentation of the coronary arteries.