Figure 5.

Basic architecture of the U-Net used. We inserted a strided convolution (green) as the first layer (stride 2) with a large kernel (7 × 7 × 7). This modification is complemented by a transposed convolution in the last layer (yellow). This reduces greatly the need for feature map memory and significantly increases the maximum input size. Curved arrows denote residual connections. Note that there is no skip connection at the highest level.