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

doi:10.1117/12.2512168

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

Department of Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Citations (Scopus)

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	https://doi.org/10.1117/12.2512168
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

All Science Journal Classification (ASJC) codes

Electronic, Optical and Magnetic Materials
Biomaterials
Atomic and Molecular Physics, and Optics
Radiology Nuclear Medicine and imaging

Access to Document

10.1117/12.2512168

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Hong, Y., Commandeur, F., Cadet, S., Goeller, M., Doris, M. K., Chen, X., Kwiecinski, J., Berman, D. S., Slomka, P. J., Chang, H. J., & Dey, D. (2019). Deep learning-based stenosis quantification from coronary CT angiography. In E. D. Angelini, E. D. Angelini, E. D. Angelini, & B. A. Landman (Eds.), Medical Imaging 2019: Image Processing [109492I] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 10949). SPIE. https://doi.org/10.1117/12.2512168

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. / Deep learning-based stenosis quantification from coronary CT angiography. Medical Imaging 2019: Image Processing. editor / Elsa D. Angelini ; Elsa D. Angelini ; Elsa D. Angelini ; Bennett A. Landman. SPIE, 2019. (Progress in Biomedical Optics and Imaging - Proceedings of SPIE).

@inproceedings{bab9f281e9f34ed0a3fb8c79d12ef682,

title = "Deep learning-based stenosis quantification from coronary CT angiography",

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.",

author = "Youngtaek Hong and Frederic Commandeur and Sebastien Cadet and Markus Goeller and Doris, {Mhairi K.} and Xi Chen and Jacek Kwiecinski and Berman, {Daniel S.} and Slomka, {Piotr J.} and Chang, {Hyuk Jae} and Damini Dey",

year = "2019",

month = jan,

day = "1",

doi = "10.1117/12.2512168",

language = "English",

series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",

publisher = "SPIE",

editor = "Angelini, {Elsa D.} and Angelini, {Elsa D.} and Angelini, {Elsa D.} and Landman, {Bennett A.}",

booktitle = "Medical Imaging 2019",

address = "United States",

note = "Medical Imaging 2019: Image Processing ; Conference date: 19-02-2019 Through 21-02-2019",

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Hong, Y, Commandeur, F, Cadet, S, Goeller, M, Doris, MK, Chen, X, Kwiecinski, J, Berman, DS, Slomka, PJ, Chang, HJ & Dey, D 2019, Deep learning-based stenosis quantification from coronary CT angiography. in ED Angelini, ED Angelini, ED Angelini & BA Landman (eds), Medical Imaging 2019: Image Processing., 109492I, Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10949, SPIE, Medical Imaging 2019: Image Processing, San Diego, United States, 19/2/19. https://doi.org/10.1117/12.2512168

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.

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M3 - Conference contribution

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T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE

BT - Medical Imaging 2019

A2 - Angelini, Elsa D.

A2 - Landman, Bennett A.

PB - SPIE

T2 - Medical Imaging 2019: Image Processing

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