Deep learning-based stenosis quantification from coronary CT angiography

Hong Y, Commandeur F, Cadet S, Göller M, Doris MK, Chen X, Kwiecinski J, Berman DS, Slomka PJ, Chang HJ, Dey D (2019)

Publication Type: Conference contribution

Publication year: 2019

Journal

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Publisher: SPIE

Book Volume: 10949

Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Event location: San Diego, CA US

ISBN: 9781510625457

DOI: 10.1117/12.2512168

Abstract

Coronary computed tomography angiography (CTA) allows quantification of stenosis. However, such quantitative analysis is not part of clinical routine. We evaluated the feasibility of utilizing deep learning for quantifying coronary artery disease from CTA. Methods: A total of 716 diseased segments in 156 patients (66 ± 10 years) who underwent CTA were analyzed. Minimal luminal area (MLA), percent diameter stenosis (DS), and percent contrast density difference (CDD) were measured using semi-automated software (Autoplaque) by an expert reader. Using the expert annotations, deep learning was performed with convolutional neural networks using 10-fold cross-validation to segment CTA lumen and calcified plaque. MLA, DS and CDD computed using deep-learning-based approach was compared to expert reader measurements. Results: There was excellent correlation between the expert reader and deep learning for all quantitative measures (r=0.984 for MLA; r=0.957 for DS; and r=0.975 for CDD, p<0.001 for all). The expert reader and deep learning method was not significantly different for MLA (median 4.3 mm2 for both, p=0.68) and CDD (11.6 vs 11.1%, p=0.30), and was significantly different for DS (26.0 vs 26.6%, p<0.05); however, the ranges of all the quantitative measures were within inter-observer variability between 2 expert readers. Conclusions: Our deep learning-based method allows quantitative measurement of coronary artery disease segments accurately from CTA and may enhance clinical reporting.

Authors with CRIS profile

Markus Göller Department of Medicine 2 – Cardiology and Angiology

Involved external institutions

Cedars-Sinai Medical Center US United States (USA) (US) Severance Hospital / 세브란스 병원 KR Korea, Republic of (KR) Yonsei University KR Korea, Republic of (KR)

How to cite

APA:

Hong, Y., Commandeur, F., Cadet, S., Göller, M., Doris, M.K., Chen, X.,... Dey, D. (2019). Deep learning-based stenosis quantification from coronary CT angiography. In Bennett A. Landman, Elsa D. Angelini, Elsa D. Angelini, Elsa D. Angelini (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. San Diego, CA, US: SPIE.

MLA:

Hong, Youngtaek, et al. "Deep learning-based stenosis quantification from coronary CT angiography." Proceedings of the Medical Imaging 2019: Image Processing, San Diego, CA Ed. Bennett A. Landman, Elsa D. Angelini, Elsa D. Angelini, Elsa D. Angelini, SPIE, 2019.

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