Figure 2:

The convolution neural network architecture. The inputs are 3D brain volumes. Each cube represents one 3D feature map. The size of the cube reflects the spatial dimension of the feature map, the number of the cubes reflects the number of feature maps (channel dimension) at a specific depth. The blue arrow denotes 3D convolution operation with rectified linear unit (ReLU), the green arrow denotes 3D convolution followed by batch normalization (BN) and ReLU, the yellow arrow denotes the max pooling operation. The basic unit enclosed in the bracket is repeated N = 5 times with increasing number of features and decreasing spatial dimension. The final convolutional output is flattened and fed into one fully-connected (FC) layer with linear output (red arrow), generating the final brain age prediction.