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Deep Learning-Based Stenosis Quantification From Coronary CT Angiography

 Youngtaek Hong  ;  Frederic Commandeur  ;  Sebastien Cadet  ;  Markus Goeller  ;  Mhairi K Doris  ;  Xi Chen  ;  Jacek Kwiecinski  ;  Daniel S Berman  ;  Piotr J Slomka  ;  Hyuk-Jae Chang  ;  Damini Dey 
 Proceedings of SPIE--the International Society for Optical Engineering, Vol.10949 : 109492I, 2019-02 
Journal Title
 Proceedings of SPIE--the International Society for Optical Engineering 
Issue Date
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.
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1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
5. Research Institutes (연구소) > Others (기타) > 1. Journal Papers
Yonsei Authors
Chang, Hyuk-Jae(장혁재) ORCID logo https://orcid.org/0000-0002-6139-7545
Hong, Youngtaek(홍영택)
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