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.
| Original language | English |
|---|---|
| Title of host publication | Medical Imaging 2019 |
| Subtitle of host publication | Image Processing |
| Editors | Elsa D. Angelini, Elsa D. Angelini, Elsa D. Angelini, Bennett A. Landman |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510625457 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
| Event | Medical Imaging 2019: Image Processing - San Diego, United States Duration: 2019 Feb 19 → 2019 Feb 21 |
Publication series
| Name | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
|---|---|
| Volume | 10949 |
| ISSN (Print) | 1605-7422 |
Conference
| Conference | Medical Imaging 2019: Image Processing |
|---|---|
| Country | United States |
| City | San Diego |
| Period | 19/2/19 → 19/2/21 |
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All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Biomaterials
- Atomic and Molecular Physics, and Optics
- Radiology Nuclear Medicine and imaging
Cite this
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Deep learning-based stenosis quantification from coronary CT angiography. / Hong, Youngtaek; Commandeur, Frederic; Cadet, Sebastien; Goeller, Markus; Doris, Mhairi K.; Chen, Xi; Kwiecinski, Jacek; Berman, Daniel S.; Slomka, Piotr J.; Chang, Hyuk Jae; Dey, Damini.
Medical Imaging 2019: Image Processing. ed. / Elsa D. Angelini; Elsa D. Angelini; Elsa D. Angelini; Bennett A. Landman. SPIE, 2019. 109492I (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10949).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Deep learning-based stenosis quantification from coronary CT angiography
AU - Hong, Youngtaek
AU - Commandeur, Frederic
AU - Cadet, Sebastien
AU - Goeller, Markus
AU - Doris, Mhairi K.
AU - Chen, Xi
AU - Kwiecinski, Jacek
AU - Berman, Daniel S.
AU - Slomka, Piotr J.
AU - Chang, Hyuk Jae
AU - Dey, Damini
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85068309283&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068309283&partnerID=8YFLogxK
U2 - 10.1117/12.2512168
DO - 10.1117/12.2512168
M3 - Conference contribution
AN - SCOPUS:85068309283
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2019
A2 - Angelini, Elsa D.
A2 - Angelini, Elsa D.
A2 - Angelini, Elsa D.
A2 - Landman, Bennett A.
PB - SPIE
ER -