Automatic segmentation of LV and RV in cardiac MRI
Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS …, 2018•Springer
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.
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.
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.
Springer
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