Abstract
Automatic coronary artery labeling is essential yet challenging step in coronary artery disease diagnosis for clinician. Previous methods typically overlooked rich relationships with heart chamber and also morphological features of coronary artery. In this paper, we propose a novel point-cloud learning method (called CorLab-Net), which comprehensively captures both inter-organ and intra-artery spatial dependencies as explicit guidance to assist the labeling of these challenging coronary vessels. Specifically, given a 3D point cloud extracted from the segmented coronary artery, our CorLab-Net improves artery labeling from three aspects: First, it encodes the inter-organ anatomical dependency between vessels and heart chambers (in terms of spatial distance field) to effectively locate the blood vessels. Second, it extracts the intra-artery anatomical dependency between vessel points and key joint points (in terms of morphological distance field) to precisely identify different vessel branches at the junctions. Third, it enhances the intra-artery local dependency between neighboring points (by using graph convolutional modules) to correct labeling outliers and improve consistency, especially at the vascular endings. We evaluated our method on a real-clinical dataset. Extensive experiments show that CorLab-Net significantly outperformed the state-of-the-art methods in labeling coronary arteries with large appearance-variance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Zhang, D., et al.: Direct quantification of coronary artery stenosis through hierarchical attentive multi-view learning. IEEE Trans. Med. Imaging 39(12), 4322–4334 (2020)
Chen, S.Y., Carroll, J.D., Messenger, J.C.: Quantitative analysis of reconstructed 3-D coronary arterial tree and intracoronary devices. IEEE Trans. Med. Imaging 21(7), 724–740 (2002)
Yang, G., et al.: Automatic coronary artery tree labeling in coronary computed tomographic angiography datasets. In: Computing in Cardiology, vol. 2011, pp. 109–112. IEEE (2011)
Cao, Q., et al.: Automatic identification of coronary tree anatomy in coronary computed tomography angiography. Int. J. Cardiovasc. Imaging 33(11), 1809–1819 (2017)
Wu, D., et al.: Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs. Int. J. Comput. Assist. Radiol. Surg. 14(2), 271–280 (2019)
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660 (2017)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: PointNet++ deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5105–5114 (2017)
Guo, Y., Wang, H., Hu, Q., et al.: Deep learning for 3D point clouds: a survey. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1 (2020)
Yang, X., Xia, D., Kin, T., Igarashi, T.: Surface-based 3D deep learning framework for segmentation of intracranial aneurysms from TOF-MRA images. arXiv preprint arXiv:2006.16161 (2020)
Yao, L., et al.: Graph convolutional network based point cloud for head and neck vessel labeling. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds.) MLMI 2020. LNCS, vol. 12436, pp. 474–483. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59861-7_48
Wang, S., Dai, W., Xu, M., Li, C., Zou, J., Xiong, H.: Structure-aware graph construction for point cloud segmentation with graph convolutional networks. In: 2020 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2020)
Lin, Z.H., Huang, S.Y., Wang, Y.C.F.: Convolution in the cloud: learning deformable kernels in 3D graph convolution networks for point cloud analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1800–1809 (2020)
Yang, H., Zhen, X., Chi, Y., Zhang, L., Hua, X.S.: CPR-GCN: conditional partial-residual graph convolutional network in automated anatomical labeling of coronary arteries. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3803–3811 (2020)
Fan, H., Su, H., Guibas, L.J.: A point set generation network for 3D object reconstruction from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 605–613 (2017)
Cui, Z., et al.: TsegNet: an efficient and accurate tooth segmentation network on 3D dental model. Med. Image Anal. 69, 101949 (2021)
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)
Acknowledgment
This work was supported by the National Natural Science Foundation of China under Grants 62073260.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, X., Cui, Z., Feng, J., Song, Y., Wu, D., Shen, D. (2021). CorLab-Net: Anatomical Dependency-Aware Point-Cloud Learning for Automatic Labeling of Coronary Arteries. In: Lian, C., Cao, X., Rekik, I., Xu, X., Yan, P. (eds) Machine Learning in Medical Imaging. MLMI 2021. Lecture Notes in Computer Science(), vol 12966. Springer, Cham. https://doi.org/10.1007/978-3-030-87589-3_59
Download citation
DOI: https://doi.org/10.1007/978-3-030-87589-3_59
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87588-6
Online ISBN: 978-3-030-87589-3
eBook Packages: Computer ScienceComputer Science (R0)
