Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images

Sunghee Jung; Soochahn Lee; Byunghwan Jeon; Yeonggul Jang; Hyuk Jae Chang

doi:10.1007/978-3-030-00536-8_11

Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images

Sunghee Jung, Soochahn Lee, Byunghwan Jeon, Yeonggul Jang, Hyuk Jae Chang

Department of Internal Medicine

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

3 Citations (Scopus)

Abstract

Motion artifact compensation of the coronary artery in computed tomography (CT) is required to quantify the risk of coronary artery disease more accurately. We present a novel method based on deep learning for motion artifact compensation in coronary CT angiography (CCTA). The ground-truth, i.e., coronary artery without motion, was synthesized using full-phase four-dimensional (4D) CT by applying style-transfer method because it is medically impossible to obtain in practice. The network for motion artifact compensation based on very deep convolutional neural network (CNN) is trained using the synthesized ground-truth. An observer study was performed for the evaluation of the proposed method. The motion artifacts were markedly reduced and boundaries of the coronary artery were much sharper than before applying the proposed method, with a strong inter-observer agreement (kappaÂ =Â 0.78).

Original language	English
Title of host publication	Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings
Editors	Orcun Goksel, Ipek Oguz, Ali Gooya, Ninon Burgos
Publisher	Springer Verlag
Pages	100-110
Number of pages	11
ISBN (Print)	9783030005351
DOIs	https://doi.org/10.1007/978-3-030-00536-8_11
Publication status	Published - 2018
Event	3rd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 2018 Sept 16 → 2018 Sept 16

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11037 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	3rd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	18/9/16 → 18/9/16

Bibliographical note

Funding Information:
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (2012027176) and (NRF-2015R1C1A1A01054697).

Funding Information:
Acknowledgment. This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (2012027176) and (NRF-2015R1C1A1A01054697).

Publisher Copyright:
© 2018, Springer Nature Switzerland AG.

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-030-00536-8_11

Cite this

Jung, S., Lee, S., Jeon, B., Jang, Y., & Chang, H. J. (2018). Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images. In O. Goksel, I. Oguz, A. Gooya, & N. Burgos (Eds.), Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 100-110). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11037 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-00536-8_11

Jung, Sunghee ; Lee, Soochahn ; Jeon, Byunghwan et al. / Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images. Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. editor / Orcun Goksel ; Ipek Oguz ; Ali Gooya ; Ninon Burgos. Springer Verlag, 2018. pp. 100-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images",

abstract = "Motion artifact compensation of the coronary artery in computed tomography (CT) is required to quantify the risk of coronary artery disease more accurately. We present a novel method based on deep learning for motion artifact compensation in coronary CT angiography (CCTA). The ground-truth, i.e., coronary artery without motion, was synthesized using full-phase four-dimensional (4D) CT by applying style-transfer method because it is medically impossible to obtain in practice. The network for motion artifact compensation based on very deep convolutional neural network (CNN) is trained using the synthesized ground-truth. An observer study was performed for the evaluation of the proposed method. The motion artifacts were markedly reduced and boundaries of the coronary artery were much sharper than before applying the proposed method, with a strong inter-observer agreement (kappa{\^A} ={\^A} 0.78).",

author = "Sunghee Jung and Soochahn Lee and Byunghwan Jeon and Yeonggul Jang and Chang, {Hyuk Jae}",

note = "Funding Information: This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (2012027176) and (NRF-2015R1C1A1A01054697). Funding Information: Acknowledgment. This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (2012027176) and (NRF-2015R1C1A1A01054697). Publisher Copyright: {\textcopyright} 2018, Springer Nature Switzerland AG.; 3rd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 16-09-2018",

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Jung, S, Lee, S, Jeon, B, Jang, Y & Chang, HJ 2018, Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images. in O Goksel, I Oguz, A Gooya & N Burgos (eds), Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11037 LNCS, Springer Verlag, pp. 100-110, 3rd International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2018 Held in Conjunction with 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain, 18/9/16. https://doi.org/10.1007/978-3-030-00536-8_11

Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images. / Jung, Sunghee; Lee, Soochahn; Jeon, Byunghwan et al.
Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. ed. / Orcun Goksel; Ipek Oguz; Ali Gooya; Ninon Burgos. Springer Verlag, 2018. p. 100-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11037 LNCS).

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

TY - GEN

T1 - Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images

AU - Jung, Sunghee

AU - Lee, Soochahn

AU - Jeon, Byunghwan

AU - Jang, Yeonggul

AU - Chang, Hyuk Jae

N1 - Funding Information: This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (2012027176) and (NRF-2015R1C1A1A01054697). Funding Information: Acknowledgment. This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (MSIP) (2012027176) and (NRF-2015R1C1A1A01054697). Publisher Copyright: © 2018, Springer Nature Switzerland AG.

PY - 2018

Y1 - 2018

N2 - Motion artifact compensation of the coronary artery in computed tomography (CT) is required to quantify the risk of coronary artery disease more accurately. We present a novel method based on deep learning for motion artifact compensation in coronary CT angiography (CCTA). The ground-truth, i.e., coronary artery without motion, was synthesized using full-phase four-dimensional (4D) CT by applying style-transfer method because it is medically impossible to obtain in practice. The network for motion artifact compensation based on very deep convolutional neural network (CNN) is trained using the synthesized ground-truth. An observer study was performed for the evaluation of the proposed method. The motion artifacts were markedly reduced and boundaries of the coronary artery were much sharper than before applying the proposed method, with a strong inter-observer agreement (kappaÂ =Â 0.78).

AB - Motion artifact compensation of the coronary artery in computed tomography (CT) is required to quantify the risk of coronary artery disease more accurately. We present a novel method based on deep learning for motion artifact compensation in coronary CT angiography (CCTA). The ground-truth, i.e., coronary artery without motion, was synthesized using full-phase four-dimensional (4D) CT by applying style-transfer method because it is medically impossible to obtain in practice. The network for motion artifact compensation based on very deep convolutional neural network (CNN) is trained using the synthesized ground-truth. An observer study was performed for the evaluation of the proposed method. The motion artifacts were markedly reduced and boundaries of the coronary artery were much sharper than before applying the proposed method, with a strong inter-observer agreement (kappaÂ =Â 0.78).

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DO - 10.1007/978-3-030-00536-8_11

M3 - Conference contribution

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SN - 9783030005351

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 110

BT - Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings

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A2 - Gooya, Ali

A2 - Burgos, Ninon

PB - Springer Verlag

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ER -

Jung S, Lee S, Jeon B, Jang Y, Chang HJ. Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images. In Goksel O, Oguz I, Gooya A, Burgos N, editors, Simulation and Synthesis in Medical Imaging - Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Proceedings. Springer Verlag. 2018. p. 100-110. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-00536-8_11

Deep learning based coronary artery motion artifact compensation using style-transfer synthesis in ct images

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