Skip to main content
. Author manuscript; available in PMC: 2019 Sep 5.
Published in final edited form as: IEEE Trans Med Imaging. 2019 Jan 23;38(9):2151–2164. doi: 10.1109/TMI.2019.2894322

Figure 4.

Figure 4

The architecture of the proposed SSLLN with 15 convolutional layers. The network takes different CMR volumes as input, applies a branch of convolutions, learns image features from fine to coarse levels, concatenates multi-scale features and finally predicts the probability maps of segmentation and landmarks simultaneously. These probability maps, together with the ground-truth segmentation labels and landmark locations, are then utilised in the loss function in (1) which is minimised via the stochastic gradient descent. Here #S, #A, #C, #LK and GT represent the number of volume slices, the number of activation maps, the number of anatomies, the number of landmarks, and ground truth, respectively.