A Vessel-Focused 3D Convolutional Network for Automatic Segmentation and Classification of Coronary Artery Plaques in Cardiac CTA

The segmentation and classification of atherosclerotic plaque (AP) are of great importance in the diagnosis and treatment of coronary artery disease. Although the constitution of AP can be assessed through a contrast-enhanced coronary computed tomography angiography (CCTA), the interpretation of CCTA scans is time-consuming and tedious for radiologists. Automation of AP segmentation is highly desired for clinical applications and further researches. However, it is difficult due to the extreme unbalance of voxels, similar appearance between some plaques and background tissues, and artefacts. In this paper, we propose a vessel-focused 3D convolutional network for automatic segmentation of AP including three subtypes: calcified plaques (CAP), non-calcified plaques (NCAP) and mixed calcified plaques (MCAP). We first extract the coronary arteries from the CT volumes; then we reform the artery segments into straightened volumes; finally, a 3D vessel-focused convolutional neural network is employed for plaque segmentation. The proposed method is trained and tested on a dataset of multi-phase CCTA volumes of 25 patients. We further investigate the effect of artery straightening through a comparison experiment, in which the network is trained on original CT volumes. Results show that by artery extraction and straightening, the training time is reduced by 40% and the segmentation performance of non-calcified plaques and mixed calcified plaques gains significantly. The proposed method achieves dice scores of 0.83, 0.73 and 0.68 for CAP, NCAP and MCAP respectively on the test set, which shows potential value for clinical application.