U-NET

Network Architecture

The network architecture is illustrated in Figure. It consists of a contracting path (left side) and an expansive path (right side).

The contracting path follows the typical architecture of a convolutional network. It consists of:

• Convolutions (kernel size = 3x3, stride = 1 padding = 1)
• Batch Normalization (added in here, does not exist in original implementation)
• ReLU
• Convolutions (kernel size = 3x3, stride = 1 padding = 1)
• Batch Normalization (added in here, does not exist in original implementation)
• ReLU
• Downsampling: Max pooling operation (kernel size = 2x2, stride = 2)

Every step in the expansive path consists of:

• Upsampling: Transpose Convolution (kernel size = 2x2, stride = 2, padding = 0)
• Convolutions (filter size = 2x2)
• Concatenation with the correspondingly cropped feature map from the contracting path
• Convolutions (kernel size = 3x3)
• ReLU
• Convolutions (kernel size = 3x3)
• ReLU

At the final layer a 1x1 convolution is used to map each 64-component feature vector to the desired number of classes. In total the network has 23 convolutional layers.