Abstract
The total volume of Epicardial Adipose Tissue (EAT) is a well-known independent early marker of coronary heart disease. Though several deep learning methods were proposed for CT-based EAT volume estimation with promising results recently, automatic EAT quantification on screening Low-Dose CT (LDCT) has not been studied. We first systematically investigate a deep-learning-based approach for EAT quantification on challenging noisy LDCT images using a large dataset consisting of 493 LDCT and 154 CT studies from 569 subjects. Our results demonstrate that (1) 3D U-net precisely segment the pericardium interior region (Dice score \(0.95\pm 0.00\)); (2) postprocessing based on narrow 1-mm Gaussian filter does not require adjustments of EAT Hounsfield interval and leads to accurate estimation of EAT volume (Pearson’s R \(0.96\, {\pm }\, 0.01\)) comparing to CT-based manual EAT assessment for the same subjects.
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
Ambrose, J.A., Singh, M.: Pathophysiology of coronary artery disease leading to acute coronary syndromes. F1000prime reports 7 (2015)
Commandeur, F., et al.: Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT. IEEE Trans. Med. Imaging 37(8), 1835–1846 (2018)
Commandeur, F., et al.: Fully automated CT quantification of epicardial adipose tissue by deep learning: a multicenter study. Radiol.: Artif. Intell. 1(6), e190045 (2019)
Ding, J., et al.: The association of pericardial fat with incident coronary heart disease: the multi-ethnic study of atherosclerosis (MESA). Am. J. Clin. Nutr. 90(3), 499–504 (2009)
Flüchter, S., et al.: Volumetric assessment of epicardial adipose tissue with cardiovascular magnetic resonance imaging. Obesity 15(4), 870–878 (2007)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
He, X., et al.: Automatic epicardial fat segmentation in cardiac CT imaging using 3D deep attention U-Net. In: Medical Imaging 2020: Image Processing. vol. 11313, p. 113132D. International Society for Optics and Photonics (2020)
Khan, M.A., et al.: Global epidemiology of ischemic heart disease: results from the global burden of disease study. Cureus 12(7) (2020)
Kim, B.J., et al.: Relationship of echocardiographic epicardial fat thickness and epicardial fat volume by computed tomography with coronary artery calcification: data from the Caesar study. Arch. Med. Res. 48(4), 352–359 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Lee, K.C., Yong, H.S., Lee, J., Kang, E.Y., Na, J.O.: Is the epicardial adipose tissue area on non-ECG gated low-dose chest CT useful for predicting coronary atherosclerosis in an asymptomatic population considered for lung cancer screening? Eur. Radiol. 29(2), 932–940 (2019)
Marwan, M., et al.: Quantification of epicardial adipose tissue by cardiac CT: influence of acquisition parameters and contrast enhancement. Eur. J. Radiol. 121, 108732 (2019)
Militello, C., et al.: A semi-automatic approach for epicardial adipose tissue segmentation and quantification on cardiac CT scans. Comput. Biol. Med. 114, 103424 (2019)
Milletari, F., Navab, N., Ahmadi, S.A.: V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth International Conference on 3D Vision (3DV), pp. 565–571. IEEE (2016)
Miyazawa, I., et al.: Change in pericardial fat volume and cardiovascular risk factors in a general population of Japanese men. Circul. J. CJ-18 (2018)
Morozov, S., et al.: Moscow screening: lung cancer screening with low-dose computed tomography. Problemy sotsial’noi gigieny, zdravookhraneniia i istorii meditsiny 27(Special Issue), 630–636 (2019)
Nagayama, Y., et al.: Epicardial fat volume measured on nongated chest CT is a predictor of coronary artery disease. Eur. Radiol. 29(7), 3638–3646 (2019)
Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015)
Simon-Yarza, I., Viteri-Ramírez, G., Saiz-Mendiguren, R., Slon-Roblero, P.J., Paramo, M., Bastarrika, G.: Feasibility of epicardial adipose tissue quantification in non-ECG-gated low-radiation-dose CT: comparison with prospectively ECG-gated cardiac CT. Acta Radiol. 53(5), 536–540 (2012)
Zacharov, I., et al.: ‘Zhores’-petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo institute of science and technology. Open Eng. 9(1), 512–520 (2019)
Acknowledgments
This research was supported by the Russian Science Foundation grant 20-71-10134. Computational experiments were powered by Zhores, a super computer at Skolkovo Institute of Science and Technology [20].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Goncharov, M., Chernina, V., Pisov, M., Gombolevskiy, V., Morozov, S., Belyaev, M. (2022). Quantification of Epicardial Adipose Tissue in Low-Dose Computed Tomography Images. In: Su, R., Zhang, YD., Liu, H. (eds) Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2021). MICAD 2021. Lecture Notes in Electrical Engineering, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-16-3880-0_11
Download citation
DOI: https://doi.org/10.1007/978-981-16-3880-0_11
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3879-4
Online ISBN: 978-981-16-3880-0
eBook Packages: EngineeringEngineering (R0)