Figure 3:

The basic 3D FilterNet+ architecture. The input $X$ is a 256×256×24 3D image patch cropped from the whole 256×512×30 3D pre-processed image of one leg. The size of output ${\widehat{Y}}_p$ is 6×256×256×24. The segmentations are optimized by $L_{dice}$, cross-entropy $L_{CE}$ and edge constraints $L_e$. The trainable edge gate learns the muscle compartment boundary-related parameter $\sigma$ from $\widehat{Y}$ and $Y$, used later as an image-learned component of the LOGISMOS cost function (Section II.C. and Fig. 4). Best viewed in color.