Deep Learning Combined with Radiologist’s Intervention Achieves Accurate Segmentation of Hepatocellular Carcinoma in Dual-Phase Magnetic Resonance Images

Figure 2

Architecture of the proposed deep convolutional neural networks (DCNNs). The proposed network includes two phases, contracting path (the upper and middle part of the network) and the expansive path (the lower part of the network). The contracting path is composed of six convolutional (conv) blocks (a block consists of the repeated application of a conv, batch normalization, and ReLU), three max pooling layers, and two conv layers. Every two conv blocks are followed by a max pooling layer with stride two. The expansive path is composed of three upsampling blocks (deconv block). Each Deconv Block consists of an upsampling of the feature map followed by a deconv, a concatenation with the correspondingly cropped feature map from the contracting path, and two convs.