Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks

Jelmer M. Wolterink; Tim Leiner; Max A. Viergever; Ivana Išgum

doi:10.1007/978-3-319-24553-9_72

Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks

Jelmer M. Wolterink, Tim Leiner, Max A. Viergever, Ivana Išgum

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

54 Citations (Scopus)

Abstract

The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. Non-contrast enhanced cardiac CT is considered a reference for quantification of CAC. Recently, it has been shown that CAC may be quantified in cardiac CT angiography (CCTA). We present a pattern recognition method that automatically identifies and quantifies CAC in CCTA. The study included CCTA scans of 50 patients equally distributed over five cardiovascular risk categories. CAC in CCTA was identified in two stages. In the first stage, potential CAC voxels were identified using a convolutional neural network (CNN). In the second stage, candidate CAC lesions were extracted based on the CNN output for analyzed voxels and thereafter described with a set of features and classified using a Random Forest. Ten-fold stratified cross-validation experiments were performed. CAC volume was quantified per patient and compared with manual reference annotations in the CCTA scan. Bland-Altman bias and limits of agreement between reference and automatic annotations were -15 (-198–168) after the first stage and -3 (-86 – 79) after the second stage. The results show that CAC can be automatically identified and quantified in CCTA using the proposed method. This might obviate the need for a dedicated non-contrast-enhanced CT scan for CAC scoring, which is regularly acquired prior to a CCTA scan, and thus reduce the CT radiation dose received by patients.

Original language	English
Title of host publication	Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015
Subtitle of host publication	18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I
Publisher	Springer
Pages	589-596
Number of pages	8
ISBN (Electronic)	978-3-319-24553-9
ISBN (Print)	978-3-319-24552-2
DOIs	https://doi.org/10.1007/978-3-319-24553-9_72
Publication status	Published - 2015
Externally published	Yes
Event	18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015 - Munich, Germany Duration: 5 Oct 2015 → 9 Oct 2015 Conference number: 18

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9349
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015
Abbreviated title	MICCAI 2015
Country/Territory	Germany
City	Munich
Period	5/10/15 → 9/10/15

Keywords

Automatic coronary artery calcium scoring
Cardiac CTA
Convolutional neural network
Random Forest

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-319-24553-9_72

Cite this

Wolterink, J. M., Leiner, T., Viergever, M. A., & Išgum, I. (2015). Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. In Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I (pp. 589-596). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9349). Springer. https://doi.org/10.1007/978-3-319-24553-9_72

Wolterink, Jelmer M. ; Leiner, Tim ; Viergever, Max A. et al. / Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer, 2015. pp. 589-596 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. Non-contrast enhanced cardiac CT is considered a reference for quantification of CAC. Recently, it has been shown that CAC may be quantified in cardiac CT angiography (CCTA). We present a pattern recognition method that automatically identifies and quantifies CAC in CCTA. The study included CCTA scans of 50 patients equally distributed over five cardiovascular risk categories. CAC in CCTA was identified in two stages. In the first stage, potential CAC voxels were identified using a convolutional neural network (CNN). In the second stage, candidate CAC lesions were extracted based on the CNN output for analyzed voxels and thereafter described with a set of features and classified using a Random Forest. Ten-fold stratified cross-validation experiments were performed. CAC volume was quantified per patient and compared with manual reference annotations in the CCTA scan. Bland-Altman bias and limits of agreement between reference and automatic annotations were -15 (-198–168) after the first stage and -3 (-86 – 79) after the second stage. The results show that CAC can be automatically identified and quantified in CCTA using the proposed method. This might obviate the need for a dedicated non-contrast-enhanced CT scan for CAC scoring, which is regularly acquired prior to a CCTA scan, and thus reduce the CT radiation dose received by patients.",

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author = "Wolterink, {Jelmer M.} and Tim Leiner and Viergever, {Max A.} and Ivana I{\v s}gum",

year = "2015",

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Wolterink, JM, Leiner, T, Viergever, MA & Išgum, I 2015, Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. in Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9349, Springer, pp. 589-596, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, Munich, Germany, 5/10/15. https://doi.org/10.1007/978-3-319-24553-9_72

Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. / Wolterink, Jelmer M.; Leiner, Tim; Viergever, Max A. et al.
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer, 2015. p. 589-596 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9349).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › Academic › peer-review

TY - CHAP

T1 - Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks

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AU - Leiner, Tim

AU - Viergever, Max A.

AU - Išgum, Ivana

N1 - Conference code: 18

PY - 2015

Y1 - 2015

N2 - The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. Non-contrast enhanced cardiac CT is considered a reference for quantification of CAC. Recently, it has been shown that CAC may be quantified in cardiac CT angiography (CCTA). We present a pattern recognition method that automatically identifies and quantifies CAC in CCTA. The study included CCTA scans of 50 patients equally distributed over five cardiovascular risk categories. CAC in CCTA was identified in two stages. In the first stage, potential CAC voxels were identified using a convolutional neural network (CNN). In the second stage, candidate CAC lesions were extracted based on the CNN output for analyzed voxels and thereafter described with a set of features and classified using a Random Forest. Ten-fold stratified cross-validation experiments were performed. CAC volume was quantified per patient and compared with manual reference annotations in the CCTA scan. Bland-Altman bias and limits of agreement between reference and automatic annotations were -15 (-198–168) after the first stage and -3 (-86 – 79) after the second stage. The results show that CAC can be automatically identified and quantified in CCTA using the proposed method. This might obviate the need for a dedicated non-contrast-enhanced CT scan for CAC scoring, which is regularly acquired prior to a CCTA scan, and thus reduce the CT radiation dose received by patients.

AB - The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. Non-contrast enhanced cardiac CT is considered a reference for quantification of CAC. Recently, it has been shown that CAC may be quantified in cardiac CT angiography (CCTA). We present a pattern recognition method that automatically identifies and quantifies CAC in CCTA. The study included CCTA scans of 50 patients equally distributed over five cardiovascular risk categories. CAC in CCTA was identified in two stages. In the first stage, potential CAC voxels were identified using a convolutional neural network (CNN). In the second stage, candidate CAC lesions were extracted based on the CNN output for analyzed voxels and thereafter described with a set of features and classified using a Random Forest. Ten-fold stratified cross-validation experiments were performed. CAC volume was quantified per patient and compared with manual reference annotations in the CCTA scan. Bland-Altman bias and limits of agreement between reference and automatic annotations were -15 (-198–168) after the first stage and -3 (-86 – 79) after the second stage. The results show that CAC can be automatically identified and quantified in CCTA using the proposed method. This might obviate the need for a dedicated non-contrast-enhanced CT scan for CAC scoring, which is regularly acquired prior to a CCTA scan, and thus reduce the CT radiation dose received by patients.

KW - Automatic coronary artery calcium scoring

KW - Cardiac CTA

KW - Convolutional neural network

KW - Random Forest

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Wolterink JM, Leiner T, Viergever MA, Išgum I. Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks. In Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I. Springer. 2015. p. 589-596. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-24553-9_72

Automatic coronary calcium scoring in cardiac CT angiography using convolutional neural networks

Abstract

Publication series

Conference

Keywords

UN SDGs

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