Automatic segmentation of LV and RV in cardiac MRI

Yeonggul Jang; Yoonmi Hong; Seongmin Ha; Sekeun Kim; Hyuk Jae Chang

doi:10.1007/978-3-319-75541-0_17

Automatic segmentation of LV and RV in cardiac MRI

Yeonggul Jang, Yoonmi Hong, Seongmin Ha, Sekeun Kim, Hyuk Jae Chang

Department of Internal Medicine

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

13 Citations (Scopus)

Abstract

Automatic and accurate segmentation of Left Ventricle (LV) and Right Ventricle (RV) in cine-MRI is required to analyze cardiac function and viability. We present a fully convolutional neural network to efficiently segment LV and RV as well as myocardium. The network is trained end-to-end from scratch. Average dice scores from five-fold cross-validation on the ACDC training dataset were 0.94, 0.89, and 0.88 for LV, RV, and myocardium. Experimental results show the robustness of the proposed architecture.

Original language	English
Title of host publication	Statistical Atlases and Computational Models of the Heart
Subtitle of host publication	ACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers
Editors	Olivier Bernard, Pierre-Marc Jodoin, Xiahai Zhuang, Guang Yang, Alistair Young, Maxime Sermesant, Alain Lalande, Mihaela Pop
Publisher	Springer Verlag
Pages	161-169
Number of pages	9
ISBN (Print)	9783319755403
DOIs	https://doi.org/10.1007/978-3-319-75541-0_17
Publication status	Published - 2018 Jan 1
Event	8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017 - Quebec City, Canada Duration: 2017 Sep 10 → 2017 Sep 14

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10663 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017
Country	Canada
City	Quebec City
Period	17/9/10 → 17/9/14

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-75541-0_17

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Cite this

Jang, Y., Hong, Y., Ha, S., Kim, S., & Chang, H. J. (2018). Automatic segmentation of LV and RV in cardiac MRI. In O. Bernard, P-M. Jodoin, X. Zhuang, G. Yang, A. Young, M. Sermesant, A. Lalande, & M. Pop (Eds.), Statistical Atlases and Computational Models of the Heart: ACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers (pp. 161-169). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10663 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-75541-0_17

Jang, Yeonggul ; Hong, Yoonmi ; Ha, Seongmin ; Kim, Sekeun ; Chang, Hyuk Jae. / Automatic segmentation of LV and RV in cardiac MRI. Statistical Atlases and Computational Models of the Heart: ACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers. editor / Olivier Bernard ; Pierre-Marc Jodoin ; Xiahai Zhuang ; Guang Yang ; Alistair Young ; Maxime Sermesant ; Alain Lalande ; Mihaela Pop. Springer Verlag, 2018. pp. 161-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Automatic and accurate segmentation of Left Ventricle (LV) and Right Ventricle (RV) in cine-MRI is required to analyze cardiac function and viability. We present a fully convolutional neural network to efficiently segment LV and RV as well as myocardium. The network is trained end-to-end from scratch. Average dice scores from five-fold cross-validation on the ACDC training dataset were 0.94, 0.89, and 0.88 for LV, RV, and myocardium. Experimental results show the robustness of the proposed architecture.",

author = "Yeonggul Jang and Yoonmi Hong and Seongmin Ha and Sekeun Kim and Chang, {Hyuk Jae}",

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Jang, Y, Hong, Y, Ha, S, Kim, S & Chang, HJ 2018, Automatic segmentation of LV and RV in cardiac MRI. in O Bernard, P-M Jodoin, X Zhuang, G Yang, A Young, M Sermesant, A Lalande & M Pop (eds), Statistical Atlases and Computational Models of the Heart: ACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10663 LNCS, Springer Verlag, pp. 161-169, 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017, Quebec City, Canada, 17/9/10. https://doi.org/10.1007/978-3-319-75541-0_17

Automatic segmentation of LV and RV in cardiac MRI. / Jang, Yeonggul; Hong, Yoonmi; Ha, Seongmin; Kim, Sekeun; Chang, Hyuk Jae.

Statistical Atlases and Computational Models of the Heart: ACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers. ed. / Olivier Bernard; Pierre-Marc Jodoin; Xiahai Zhuang; Guang Yang; Alistair Young; Maxime Sermesant; Alain Lalande; Mihaela Pop. Springer Verlag, 2018. p. 161-169 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10663 LNCS).

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

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T1 - Automatic segmentation of LV and RV in cardiac MRI

AU - Jang, Yeonggul

AU - Hong, Yoonmi

AU - Ha, Seongmin

AU - Kim, Sekeun

AU - Chang, Hyuk Jae

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Automatic and accurate segmentation of Left Ventricle (LV) and Right Ventricle (RV) in cine-MRI is required to analyze cardiac function and viability. We present a fully convolutional neural network to efficiently segment LV and RV as well as myocardium. The network is trained end-to-end from scratch. Average dice scores from five-fold cross-validation on the ACDC training dataset were 0.94, 0.89, and 0.88 for LV, RV, and myocardium. Experimental results show the robustness of the proposed architecture.

AB - Automatic and accurate segmentation of Left Ventricle (LV) and Right Ventricle (RV) in cine-MRI is required to analyze cardiac function and viability. We present a fully convolutional neural network to efficiently segment LV and RV as well as myocardium. The network is trained end-to-end from scratch. Average dice scores from five-fold cross-validation on the ACDC training dataset were 0.94, 0.89, and 0.88 for LV, RV, and myocardium. Experimental results show the robustness of the proposed architecture.

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SN - 9783319755403

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

SP - 161

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BT - Statistical Atlases and Computational Models of the Heart

A2 - Bernard, Olivier

A2 - Jodoin, Pierre-Marc

A2 - Zhuang, Xiahai

A2 - Yang, Guang

A2 - Young, Alistair

A2 - Sermesant, Maxime

A2 - Lalande, Alain

A2 - Pop, Mihaela

PB - Springer Verlag

T2 - 8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017

Y2 - 10 September 2017 through 14 September 2017

ER -

Jang Y, Hong Y, Ha S, Kim S, Chang HJ. Automatic segmentation of LV and RV in cardiac MRI. In Bernard O, Jodoin P-M, Zhuang X, Yang G, Young A, Sermesant M, Lalande A, Pop M, editors, Statistical Atlases and Computational Models of the Heart: ACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers. Springer Verlag. 2018. p. 161-169. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-75541-0_17