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.
Y. Jang and Y. Hong—both authors contributed equally.
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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).
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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: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_17
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DOI: https://doi.org/10.1007/978-3-319-75541-0_17
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