Abstract
Cardiovascular disease caused by coronary artery disease (CAD) is one of the most common causes of death worldwide. Coronary artery segmentation has attracted increasing attention since it is useful for better visualization and diagnosis. Conventional lumen segmentation methods basically describe vessels by a rough tubular model, thus presenting inferiority on abnormal vascular structures and failing to distinguish exact coronary arteries from vessel-like structures. In this paper, we propose a context aware 3D fully convolutional network (FCN) for vessel enhancement and segmentation in coronary computed tomography angiography (CTA) volumes. Combining the superior capacity of CNN in extracting discriminative features and satisfactory suppression of vessel-like structures by spatial prior knowledge embedded, the proposed approach significantly outperforms conventional Hessian vesselness based approach on a dataset of 50 coronary CTA volumes.
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Notes
- 1.
Public implementation by Kroon, D.J. https://ww2.mathworks.cn/matlabcentral/fileexchange/24409-hessian-based-frangi-vesselness-filter.
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Acknowledgement
This work is supported by the National Natural Science Foundation of China under Grant 61622207.
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Duan, Y., Feng, J., Lu, J., Zhou, J. (2019). Context Aware 3D Fully Convolutional Networks for Coronary Artery Segmentation. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_10
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