Figure 3:

Three-dimensional (3D) convolutional neural network (CNN) architecture. As outlined in Table 1, we evaluated different combinations of input 3D volume (variable D) and feature vector depths (variable F). Although this led to differences in the resulting network, other features of architecture, such as convolutions (Conv), max pooling, and upsampling steps, were kept constant. ReLU = rectified linear unit.