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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CITATION STYLE
Jang, Y., Hong, Y., Ha, S., Kim, S., & Chang, H. J. (2018). Automatic segmentation of LV and RV in cardiac MRI. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10663 LNCS, pp. 161–169). Springer Verlag. https://doi.org/10.1007/978-3-319-75541-0_17
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