Fully Automated Segmentation of Lower Extremity Deep Vein Thrombosis Using Convolutional Neural Network

Figure 1

The architecture of the proposed convolutional neural network (CNN) model. The proposed CNN network includes two phases: encoding phase and decoding phase. The encoding phase consists of 2 Conv-Group normalization (GN)—ReLu blocks (C1-C2) and 4 pooling blocks (P1-P4). The decoding phase consists of 4 upsampling blocks (U1-U4) and one convolution layer (conv1). The output of each layer is a three-dimensional matrix with the size of h×w×d, where h and w are the length and width of the feature map, respectively, and d is the feature dimension.