Structure of a convolutional block from the W-Net. Each convolutional block of dimension N with C channels contains six 3×3 convolutional layers alternated with the dropout, followed by a max pooling layer with a 2 × 2 × 2 kernel to reduce the dimensionality, finishing with a batch normalization layer. Dropout fractions are 0.1 for the 16, 32, and 64 channel convolutional layers, 0.2 for the 128 and 256 channel convolutional layers, and 0.3 for the 512 channel convolutional layers. Concatenations for skip connections are made in the channel dimension.