Figure 1: Proposed CNN system. The long-axis CMR images are forwarded to the first CNN which localizes the region of interest. After cropping and rotation, the second CNN regresses the time-resolved mitral valve annulus landmarks from Gaussian heatmaps. Finally, the motion parameters are extracted.

Figure 2: a) Feature extraction 2D Residual and 3D convolution blocks. Each residual block consists of a spatial convolution(CONV)(3x3), Batch Normalization (BN) and Leaky Rectified Linear Units (LReLU) activation layers. The 3D block consist of double spatial and temporal CONV(3x3x3)-BN-LReLU operations. b) Localization CNN architecture based on 2-D UNet with 3 encoder-decoder blocks. c) Landmark tracking Fully CNN architecture details based on 3-D UNet.For down-sampling asymmetrical max-pooling layers were applied into temporal and spatial dimensions.