Figure 5.

The architecture of the segmentation algorithm. A fully convolutional network takes each stack of cine images as an input, applies a branch of convolutions, learns image features from fine to coarse levels, concatenates multi-scale features and finally predicts the segmentation and landmark location probability maps simultaneously. These maps, together with the ground truth landmark locations and label maps, are then used in the loss function (see Equation 1) which is minimised via stochastic gradient descent.