Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images

Sekeun Kim; Yeonggul Jang; Byunghwan Jeon; Youngtaek Hong; Hackjoon Shim; Hyukjae Chang

doi:10.1007/978-3-030-01364-6_18

Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images

Sekeun Kim
, Yeonggul Jang
, Byunghwan Jeon
, Youngtaek Hong
, Hackjoon Shim
, Hyukjae Chang

Department of Biomedical Engineering

Yonsei University
Yonsei-Cedars-Sinai Integrative Cardiac Imaging Research Center

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

40 Scopus citations

Abstract

Accurate segmentation of coronary arteries is important for the diagnosis of cardiovascular diseases. In this paper, we propose a fully convolutional neural network to efficiently delineate the boundaries of the wall and lumen of the coronary arteries using intravascular ultrasound (IVUS) images. Our network addresses multi-label segmentation of the wall and lumen areas at the same time. The primary body of the proposed network is U-shaped which contains the encoding and decoding paths to learn rich hierarchical representations. The multi-scale input layer is adapted to take a multi-scale input. We deploy a multi-label loss function with weighted pixel-wise cross-entropy to alleviate imbalance of the rate of background, wall, and lumen. The proposed method is compared with three existing methods and the segmentation results are measured on four metrics, dice similarity coefficient, Jaccard index, percentage of area difference, and Hausdorff distance on totally 38,478 IVUS images from 35 subjects.

Original language	English
Title of host publication	Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018
Editors	Su-Lin Lee, Emanuele Trucco, Lena Maier-Hein, Stefano Moriconi, Shadi Albarqouni, Pierre Jannin, Simone Balocco, Guillaume Zahnd, Diana Mateus, Zeike Taylor, Stefanie Demirci, Danail Stoyanov, Raphael Sznitman, Anne Martel, Veronika Cheplygina, Eric Granger, Luc Duong
Publisher	Springer Verlag
Pages	161-168
Number of pages	8
ISBN (Print)	9783030013639
DOIs	https://doi.org/10.1007/978-3-030-01364-6_18
State	Published - 2018
Event	7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain Duration: 16 Sep 2018 → 16 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11043 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
Country/Territory	Spain
City	Granada
Period	16/09/18 → 16/09/18

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Computer-aided diagnosis
Image segmentation
Intravascular ultrasound (IVUS)
Machine learning

Access to Document

10.1007/978-3-030-01364-6_18

Cite this

Kim, S., Jang, Y., Jeon, B., Hong, Y., Shim, H., & Chang, H. (2018). Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images. In S.-L. Lee, E. Trucco, L. Maier-Hein, S. Moriconi, S. Albarqouni, P. Jannin, S. Balocco, G. Zahnd, D. Mateus, Z. Taylor, S. Demirci, D. Stoyanov, R. Sznitman, A. Martel, V. Cheplygina, E. Granger, & L. Duong (Eds.), Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018 (pp. 161-168). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11043 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01364-6_18

Kim, Sekeun ; Jang, Yeonggul ; Jeon, Byunghwan et al. / Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images. Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018. editor / Su-Lin Lee ; Emanuele Trucco ; Lena Maier-Hein ; Stefano Moriconi ; Shadi Albarqouni ; Pierre Jannin ; Simone Balocco ; Guillaume Zahnd ; Diana Mateus ; Zeike Taylor ; Stefanie Demirci ; Danail Stoyanov ; Raphael Sznitman ; Anne Martel ; Veronika Cheplygina ; Eric Granger ; Luc Duong. Springer Verlag, 2018. pp. 161-168 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images",

abstract = "Accurate segmentation of coronary arteries is important for the diagnosis of cardiovascular diseases. In this paper, we propose a fully convolutional neural network to efficiently delineate the boundaries of the wall and lumen of the coronary arteries using intravascular ultrasound (IVUS) images. Our network addresses multi-label segmentation of the wall and lumen areas at the same time. The primary body of the proposed network is U-shaped which contains the encoding and decoding paths to learn rich hierarchical representations. The multi-scale input layer is adapted to take a multi-scale input. We deploy a multi-label loss function with weighted pixel-wise cross-entropy to alleviate imbalance of the rate of background, wall, and lumen. The proposed method is compared with three existing methods and the segmentation results are measured on four metrics, dice similarity coefficient, Jaccard index, percentage of area difference, and Hausdorff distance on totally 38,478 IVUS images from 35 subjects.",

keywords = "Computer-aided diagnosis, Image segmentation, Intravascular ultrasound (IVUS), Machine learning",

author = "Sekeun Kim and Yeonggul Jang and Byunghwan Jeon and Youngtaek Hong and Hackjoon Shim and Hyukjae Chang",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 ; Conference date: 16-09-2018 Through 16-09-2018",

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editor = "Su-Lin Lee and Emanuele Trucco and Lena Maier-Hein and Stefano Moriconi and Shadi Albarqouni and Pierre Jannin and Simone Balocco and Guillaume Zahnd and Diana Mateus and Zeike Taylor and Stefanie Demirci and Danail Stoyanov and Raphael Sznitman and Anne Martel and Veronika Cheplygina and Eric Granger and Luc Duong",

booktitle = "Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018",

address = "Germany",

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Kim, S, Jang, Y, Jeon, B, Hong, Y, Shim, H & Chang, H 2018, Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images. in S-L Lee, E Trucco, L Maier-Hein, S Moriconi, S Albarqouni, P Jannin, S Balocco, G Zahnd, D Mateus, Z Taylor, S Demirci, D Stoyanov, R Sznitman, A Martel, V Cheplygina, E Granger & L Duong (eds), Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11043 LNCS, Springer Verlag, pp. 161-168, 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, Granada, Spain, 16/09/18. https://doi.org/10.1007/978-3-030-01364-6_18

Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images. / Kim, Sekeun; Jang, Yeonggul; Jeon, Byunghwan et al.
Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018. ed. / Su-Lin Lee; Emanuele Trucco; Lena Maier-Hein; Stefano Moriconi; Shadi Albarqouni; Pierre Jannin; Simone Balocco; Guillaume Zahnd; Diana Mateus; Zeike Taylor; Stefanie Demirci; Danail Stoyanov; Raphael Sznitman; Anne Martel; Veronika Cheplygina; Eric Granger; Luc Duong. Springer Verlag, 2018. p. 161-168 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11043 LNCS).

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

TY - GEN

T1 - Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images

AU - Kim, Sekeun

AU - Jang, Yeonggul

AU - Jeon, Byunghwan

AU - Hong, Youngtaek

AU - Shim, Hackjoon

AU - Chang, Hyukjae

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

N2 - Accurate segmentation of coronary arteries is important for the diagnosis of cardiovascular diseases. In this paper, we propose a fully convolutional neural network to efficiently delineate the boundaries of the wall and lumen of the coronary arteries using intravascular ultrasound (IVUS) images. Our network addresses multi-label segmentation of the wall and lumen areas at the same time. The primary body of the proposed network is U-shaped which contains the encoding and decoding paths to learn rich hierarchical representations. The multi-scale input layer is adapted to take a multi-scale input. We deploy a multi-label loss function with weighted pixel-wise cross-entropy to alleviate imbalance of the rate of background, wall, and lumen. The proposed method is compared with three existing methods and the segmentation results are measured on four metrics, dice similarity coefficient, Jaccard index, percentage of area difference, and Hausdorff distance on totally 38,478 IVUS images from 35 subjects.

AB - Accurate segmentation of coronary arteries is important for the diagnosis of cardiovascular diseases. In this paper, we propose a fully convolutional neural network to efficiently delineate the boundaries of the wall and lumen of the coronary arteries using intravascular ultrasound (IVUS) images. Our network addresses multi-label segmentation of the wall and lumen areas at the same time. The primary body of the proposed network is U-shaped which contains the encoding and decoding paths to learn rich hierarchical representations. The multi-scale input layer is adapted to take a multi-scale input. We deploy a multi-label loss function with weighted pixel-wise cross-entropy to alleviate imbalance of the rate of background, wall, and lumen. The proposed method is compared with three existing methods and the segmentation results are measured on four metrics, dice similarity coefficient, Jaccard index, percentage of area difference, and Hausdorff distance on totally 38,478 IVUS images from 35 subjects.

KW - Computer-aided diagnosis

KW - Image segmentation

KW - Intravascular ultrasound (IVUS)

KW - Machine learning

UR - https://www.scopus.com/pages/publications/85055775048

U2 - 10.1007/978-3-030-01364-6_18

DO - 10.1007/978-3-030-01364-6_18

M3 - Conference contribution

AN - SCOPUS:85055775048

SN - 9783030013639

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

SP - 161

EP - 168

BT - Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018

A2 - Lee, Su-Lin

A2 - Trucco, Emanuele

A2 - Maier-Hein, Lena

A2 - Moriconi, Stefano

A2 - Albarqouni, Shadi

A2 - Jannin, Pierre

A2 - Balocco, Simone

A2 - Zahnd, Guillaume

A2 - Mateus, Diana

A2 - Taylor, Zeike

A2 - Demirci, Stefanie

A2 - Stoyanov, Danail

A2 - Sznitman, Raphael

A2 - Martel, Anne

A2 - Cheplygina, Veronika

A2 - Granger, Eric

A2 - Duong, Luc

PB - Springer Verlag

T2 - 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018

Y2 - 16 September 2018 through 16 September 2018

ER -

Kim S, Jang Y, Jeon B, Hong Y, Shim H, Chang H. Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images. In Lee SL, Trucco E, Maier-Hein L, Moriconi S, Albarqouni S, Jannin P, Balocco S, Zahnd G, Mateus D, Taylor Z, Demirci S, Stoyanov D, Sznitman R, Martel A, Cheplygina V, Granger E, Duong L, editors, Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis - 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018 Held in Conjunction with MICCAI 2018. Springer Verlag. 2018. p. 161-168. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-01364-6_18

Fully automatic segmentation of coronary arteries based on deep neural network in intravascular ultrasound images

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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