Fig. 2.

An illustration of the proposed label augmentor (LA) as detailed in Section III-B. For each voxel i of the input volume y, LA augments its label to a vector ${\left[y_s^i,y_b^i\right]}^T$, where $y_s^i$ is the segmentation label and $y_b^i$ is the boundary label. The gray areas indicate the same cleft. The boundary labels y_b are computed using tanh distance map (TDM) as shown in the figure. The same network with weight sharing is employed to learn predictions on the two sets of labels. The segmentation loss ${\mathcal{L}}_s$ for the volume y is achieved from the segmentation predictions ${\widehat{y}}_s$ and segmentation labels y_s. The boundary loss ${\mathcal{L}}_b$ is calculated from the boundary predictions ${\widehat{y}}_b$ and boundary labels y_b. The coherence loss ${\mathcal{L}}_c$ is computed based on the divergences between predictions ${\widehat{y}}_s$ and ${\widehat{y}}_b$.