Full Quantification of Left Ventricle Using Deep Multitask Network with Combination of 2D and 3D Convolution on 2D + t Cine MRI

Accurate quantification of left ventricle (LV) from cardiac image are valuable to evaluate ventricular function information such as stroke volume and ejection fraction. In this paper, we proposed a novel FCN architecture, which is trained in end-to-end manner, for full quantification of cardiac LV on 2D + t cine MR images. Considering 3D information as features for temporal modeling can improve performance of the model for temporal-related task. The proposed FCN with the alternate 3D-2D convolutional module addresses each sequence with assistance from adjacent sequences and shows the comparable results compared with the state-of-the-art method.

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