Abstract
Automatic and accurate coronary artery labeling technique from CCTA can greatly reduce clinician’s manual efforts and benefit large-scale data analysis. Current line of research falls into two general categories: knowledge-based methods and learning-based techniques. However, no matter in which fashion it is developed, the formation of problem finally attributes to tree-structured centerline classification and requires hand-crafted features. Here, instead we present a new concise, effective and flexible framework for automatic coronary artery labeling by modeling the task as coronary artery parsing task. An intact pipeline is proposed and two paralleled sub-modules are further designed to consume volumetric image and unordered point cloud correspondingly. Finally, a self-contained loss is proposed to supervise labeling process. At experiment section, we conduct comprehensive experiments on collected 526 CCTA scans and exhibit stable and promising results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akinyemi, A., Murphy, S., Poole, I., Roberts, C.: Automatic labelling of coronary arteries. In: 2009 17th European Signal Processing Conference, pp. 1562–1566 (2009)
Cao, Q., et al.: Automatic identification of coronary tree anatomy in coronary computed tomography angiography. Int. J. Cardiovasc. Imaging 33(11), 1809–1819 (2017). https://doi.org/10.1007/s10554-017-1169-0
Chalopin, C., Finet, G., Magnin, I.E.: Modeling the 3d coronary tree for labeling purposes. Med. Image Anal. 5(4), 301–315 (2001). https://doi.org/10.1016/S1361-8415(01)00047-0
Gülsün, M.A., Funka-Lea, G., Zheng, Y., Eckert, M.: CTA coronary labeling through efficient geodesics between trees using anatomy priors. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014. LNCS, vol. 8674, pp. 521–528. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10470-6_65
Guo, Z., et al.: DeepCenterline: a multi-task fully convolutional network for centerline extraction. In: Chung, A.C.S., Gee, J.C., Yushkevich, P.A., Bao, S. (eds.) IPMI 2019. LNCS, vol. 11492, pp. 441–453. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20351-1_34
Jin, D., Iyer, K.S., Chen, C., Hoffman, E.A., Saha, P.K.: A robust and efficient curve skeletonization algorithm for tree-like objects using minimum cost paths. Pattern Recogn. Lett. 76, 32–40 (2016)
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3d surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques. SIGGRAPH ’87, Association for Computing Machinery, New York, NY, USA, pp. 163–169 (1987), https://doi.org/10.1145/37401.37422
Metz, C.T., Schaap, M., Weustink, A.C., Mollet, N.R., van Walsum, T., Niessen, W.J.: Coronary centerline extraction from ct coronary angiography images using a minimum cost path approach. Med. Phys. 36(12), 5568–5579 (2009)
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. IEEE (2017)
Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: deep hierarchical feature learning on point sets in a metric space. arXiv preprint, (2017) arXiv:1706.02413
Sato, M., Bitter, I., Bender, M.A., Kaufman, A.E., Nakajima, M.: Teasar: tree-structure extraction algorithm for accurate and robust skeletons. In: Proceedings the Eighth Pacific Conference on Computer Graphics and Applications, pp. 281–449 (2000)
Wu, D., et al.: A learning based deformable template matching method for automatic rib centerline extraction and labeling in ct images. In 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 980–987. IEEE (2012)
Wu, D., et al.: Automated anatomical labeling of coronary arteries via bidirectional tree LSTMs. Int. J. Comput. Assist. Radiol. Surg. 14(2), 271–280 (2018). https://doi.org/10.1007/s11548-018-1884-6
Xia, Q., Yao, Y., Hu, Z., Hao, A.: Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks. In: Pop, M., et al. (eds.) STACOM 2018. LNCS, vol. 11395, pp. 211–220. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12029-0_23
Yang, G., et al.: Automatic coronary artery tree labeling in coronary computed tomographic angiography datasets. Computing in Cardiology, pp. 109–112. IEEE (2011)
Zheng, Y., Tek, H., Funka-Lea, G.: Robust and accurate coronary artery centerline extraction in CTA by combining model-driven and data-driven approaches. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013. LNCS, vol. 8151, pp. 74–81. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40760-4_10
Zhuang, X., Shen, J.: Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med. Image Anal. 31, 77–87 (2016). https://doi.org/10.1016/j.media.2016.02.006. http://www.sciencedirect.com/science/article/pii/S1361841516000219
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Li, Z. et al. (2020). Segmentation to Label: Automatic Coronary Artery Labeling from Mask Parcellation. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-59861-7_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-59860-0
Online ISBN: 978-3-030-59861-7
eBook Packages: Computer ScienceComputer Science (R0)
