Abstract
Recently, many studies indicate that epicardial adipose tissue (EAT) volume closely associates with progression of coronary atherosclerosis as well as increased prevalence of coronary artery disease. Segmentation of the epicardium is an important step for estimation of EAT volume. There are two main challenges facing automatic epicardium segmentation. One is that the epicardium exists as a very thin line and the contrast with the surrounding adipose tissue, blood vessels, and the heart is low. The other is that there are quite a few invisible parts. In this study, we propose a fully deep-learning automatic method for visible epicardium segmentation, which is developed using 2D DenseU-Net, demonstrated that it is possible to automatically segment epicardium, and we also find that the segmentation results of visible epicardium are affected by EAT volume.
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
References
Shimabukuro, M., et al.: Epicardial adipose tissue volume and adipocytokine imbalance are strongly linked to human coronary atherosclerosis. Arterioscler. Thromb. Vasc. Biol. 33, 1077–1084 (2013)
Lu, M.T., et al.: Epicardial and paracardial adipose tissue volume and attenuation. Atherosclerosis 251, 47–54 (2016)
Mahabadi, A.A., et al.: Association of pericardial fat, intrathoracic fat, and visceral abdominal fat with cardiovascular disease burden: The Framingham heart study. Eur. Heart J. 30(7), 850–856 (2009)
Commandeur, F., et al.: Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT. IEEE Trans. Med. Imaging 37, 1835–1846 (2018)
Barbosa, J.G., Figueiredo, B., Bettencourt, N., et al.: Towards automatic quantification of the epicardial fat in non-contrasted CT images. Comput. Methods Biomech. Biomed. Eng. 14(10), 905–914 (2011)
Li, X., et al.: H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans. Med. Imaging 37, 2663–2674 (2018)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015)
Huang, G., et al.: Densely connected convolutional networks (2016)
Milletari, F., Navab, N., Ahmadi, S.-A.: V-Net: fully convolutional neural networks for volumetric medical image segmentation, pp. 1–11. arXiv (2016)
Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M.J., et al. (eds.) DLMIA/ML-CDS-2017. LNCS, vol. 10553, pp. 240–248. Springer, Cham (2017)
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (2014)
Freedman, D., Zhang, T.: Interactive graph cut based segmentation with shape priors. In: IEEE Computer Society Conference on Computer Vision & Pattern Recognition (2005)
Acknowledgements
This work was supported in part by the Grant-in Aid for Scientific Research from the Japanese Ministry for Education, Science, Culture and Sports (MEXT) under the Grant No. 18H03267 and No. 17K00420.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhao, Z. et al. (2020). Automatic Segmentation of Visible Epicardium Using Deep Learning in CT Image. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_63
Download citation
DOI: https://doi.org/10.1007/978-3-030-32456-8_63
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32455-1
Online ISBN: 978-3-030-32456-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)