Automatic segmentation of LV and RV in cardiac MRI

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 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 languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart
Subtitle of host publicationACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers
EditorsOlivier Bernard, Pierre-Marc Jodoin, Xiahai Zhuang, Guang Yang, Alistair Young, Maxime Sermesant, Alain Lalande, Mihaela Pop
PublisherSpringer Verlag
Pages161-169
Number of pages9
ISBN (Print)9783319755403
DOIs
Publication statusPublished - 2018 Jan 1
Event8th 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 102017 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10663 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017
CountryCanada
CityQuebec City
Period17/9/1017/9/14

Fingerprint

Left Ventricle
Cardiac
Magnetic resonance imaging
Segmentation
Myocardium
Neural networks
Dice
Viability
Cross-validation
Fold
Neural Networks
Robustness
Experimental Results

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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.",
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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. 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 proceedingConference contribution

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T1 - Automatic segmentation of LV and RV in cardiac MRI

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AU - Hong, Yoonmi

AU - Ha, Seongmin

AU - Kim, Sekeun

AU - Chang, Hyuk-Jae

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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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Jang Y, Hong Y, Ha S, Kim S, Chang H-J. 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