Abstract
The amount of coronary artery calcium (CAC) is a strong and independent predictor of coronary heart disease (CHD). The standard routine for CAC quantification is to perform non-contrasted coronary computed-tomography (CCT) on a patient and present the resulting image to an expert, who then uses this to label CAC in a tedious and time-consuming process. To improve this situation, we present an automatic CAC labeling system with high clinical practicability. In contrast to many other automatic calcium scoring systems, it does not require additional cardiac computed tomography angiography (CCTA) data for artery-specific labeling. Instead, an atlas-based feature approach in combination with a random forest (RF) classifier is used to incorporate fuzzy spatial knowledge from offline data. Overall detection of CAC volume on a test set with 40 patients yields an \(F_1\) score of 0.95 and 1.00 accuracy for risk class assignment. The intraclass correlation coefficient is 0.98 for the left anterior descending artery (LAD), 0.88 for the left circumflex artery (LCX), and 0.98 for the right coronary artery (RCA). The implemented system offers state-of-the-art accuracy with a processing time (< 30 s) by magnitudes lower than comparable systems to be found in the literature.
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
Becker, A., Leber, A., Becker, C., Knez, A.: Predictive value of coronary calcifications for future cardiac events in asymptomatic individuals. Am. Heart J. 155(1), 154–160 (2008)
Danielsson, P.E.: Euclidean distance mapping. Comput. Graph. Image Process. 14(3), 227–248 (1980)
Go, A.S., et al.: Executive summary: heart disease and stroke statistics-2014 update. Circulation 129(3), 399–410 (2014)
Shahzad, R., van Walsum, T., Schaap, M., Rossi, A., Klein, S., Weustink, A.C., de Feyter, P.J., van Vliet, L.J., Niessen, W.J.: Vessel specific coronary artery calcium scoring: an automatic system. Acad. Radiol. 20(1), 1–9 (2013)
Wolterink, J.M., Leiner, T., Viergever, M.A., Išgum, I.: Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 589–596. Springer, Cham (2015). doi:10.1007/978-3-319-24553-9_72
Wolterink, J.M., Leiner, T., de Vos, B.D., Coatrieux, J.L., Kelm, B.M., Kondo, S., Salgado, R.A., Shahzad, R., Shu, H., Snoeren, M., Takx, R.A.P., van Vliet, L.J., van Walsum, T., Willems, T.P., Yang, G., Zheng, Y., Viergever, M.A., Isgum, I.: An evaluation of automatic coronary artery calcium scoring methods with cardiac CT using the OrCaScore framework. Med. Phys. 43(5), 2361–2373 (2016)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans. Med. Imaging 27(11), 1668–1681 (2008)
Zheng, Y., et al.: Automatic aorta segmentation and valve landmark detection in C-arm CT: application to aortic valve implantation. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6361, pp. 476–483. Springer, Heidelberg (2010). doi:10.1007/978-3-642-15705-9_58
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). doi:10.1007/978-3-642-40760-4_10
Zhong, H., Zheng, Y., Funka-Lea, G., Vega-Higuera, F.: Automatic heart isolation in 3D CT images. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds.) MCV 2012. LNCS, vol. 7766, pp. 165–180. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36620-8_17
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Durlak, F., Wels, M., Schwemmer, C., Sühling, M., Steidl, S., Maier, A. (2017). Growing a Random Forest with Fuzzy Spatial Features for Fully Automatic Artery-Specific Coronary Calcium Scoring. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-67389-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67388-2
Online ISBN: 978-3-319-67389-9
eBook Packages: Computer ScienceComputer Science (R0)