Coronary Artery Plaque Characterization from CCTA Scans Using Deep Learning and Radiomics

Denzinger F, Wels M, Ravikumar N, Breininger K, Reidelshöfer A, Eckert J, Sühling M, Schmermund A, Maier A (2019)


Publication Language: German

Publication Type: Conference contribution, Original article

Publication year: 2019

Publisher: Springer, Cham

Pages Range: 593-601

Conference Proceedings Title: MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019

Event location: Shenzhen CN

ISBN: 978-3-030-32251-9

URI: http://arxiv.org/abs/1912.06075

DOI: 10.1007/978-3-030-32251-9_65

Abstract

Assessing coronary artery plaque segments in coronary CT angiography scans is an important task to improve patient management and clinical outcomes, as it can help to decide whether invasive investigation and treatment are necessary. In this work, we present three machine learning approaches capable of performing this task. The first approach is based on radiomics, where a plaque segmentation is used to calculate various shape-, intensity- and texture-based features under different image transformations. A second approach is based on deep learning and relies on centerline extraction as sole prerequisite. In the third approach, we fuse the deep learning approach with radiomic features. On our data the methods reached similar scores as simulated fractional flow reserve (FFR) measurements, which - in contrast to our methods - requires an exact segmentation of the whole coronary tree and often time-consuming manual interaction. In literature, the performance of simulated FFR reaches an AUC between 0.79–0.93 predicting an abnormal invasive FFR that demands revascularization. The radiomics approach achieves an AUC of 0.84, the deep learning approach 0.86 and the combined method 0.88 for predicting the revascularization decision directly. While all three proposed methods can be determined within seconds, the FFR simulation typically takes several minutes. Provided representative training data in sufficient quantities, we believe that the presented methods can be used to create systems for fully automatic non-invasive risk assessment for a variety of adverse cardiac events.

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How to cite

APA:

Denzinger, F., Wels, M., Ravikumar, N., Breininger, K., Reidelshöfer, A., Eckert, J.,... Maier, A. (2019). Coronary Artery Plaque Characterization from CCTA Scans Using Deep Learning and Radiomics. In Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A. (Hrg.), MICCAI 2019: Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 (S. 593-601). Shenzhen, CN: Springer, Cham.

MLA:

Denzinger, Felix, et al. "Coronary Artery Plaque Characterization from CCTA Scans Using Deep Learning and Radiomics." Tagungsband MICCAI, Shenzhen Hrg. Shen, D., Liu, T., Peters, T.M., Staib, L.H., Essert, C., Zhou, S., Yap, P.-T., Khan, A., Springer, Cham, 2019. 593-601.

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