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Deep learning-based stenosis quantification from coronary CT angiography
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Paper Abstract

Background: 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.

Paper Details

Date Published: 15 March 2019
PDF: 9 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492I (15 March 2019); doi: 10.1117/12.2512168
Show Author Affiliations
Youngtaek Hong, Cedars-Sinai Medical Ctr. (United States)
Yonsei Univ. (Korea, Republic of)
Frederic Commandeur, Cedars-Sinai Medical Ctr. (United States)
Sebastien Cadet, Cedars-Sinai Medical Ctr. (United States)
Markus Goeller, Cedars-Sinai Medical Ctr. (United States)
Friedrich-Alexander-Univ. Erlangen-Nürnberg (Germany)
Mhairi Doris, Cedars-Sinai Medical Ctr. (United States)
The Univ. of Edinburgh (United Kingdom)
Xi Chen, Cedars-Sinai Medical Ctr. (United States)
Jacek Kwiecinski, Cedars-Sinai Medical Ctr. (United States)
The Univ. of Edinburgh (United Kingdom)
Daniel Berman, Cedars-Sinai Medical Ctr. (United States)
Piotr Slomka, Cedars-Sinai Medical Ctr. (United States)
Hyuk-Jae Chang, Yonsei Univ. (Korea, Republic of)
Damini Dey, Cedars-Sinai Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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