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 |
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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 | |
Publication status | Published - 2018 |
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) |
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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 |
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Country/Territory | Canada |
City | Quebec City |
Period | 17/9/10 → 17/9/14 |
Bibliographical note
Funding Information:Acknowledgement. This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (2017-0-00255, Autonomous digital companion framework and application).
Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)