Figure 4.

Convolutional neural network architecture. All three CNNs developed shared a common architecture and differed by the data used for training. CNNs received 2D contrast-enhanced CT images and segmentation masks as input, with input images augmented randomly during training. All convolutional layers used a kernel size of 3 × 3. A rectified linear unit (ReLU) activation function followed by batch normalization was performed at every layer. Adam optimization was used to update network weights, with parameters for alpha, beta1, beta2 and epsilon set at 0.0001, 0.9, 0.999 and 1e-08. Training was continued for 50 epochs.