Tumor Segmentation in Contrast-Enhanced Magnetic Resonance Imaging for Nasopharyngeal Carcinoma: Deep Learning with Convolutional Neural Network

Figure 1

The schematic diagram of the proposed convolutional neural network (CNN) structure. The proposed CNN network includes two phases of feature representation and scores map reconstruction. The feature representation phase consists of 2 Pool-Conv-ReLu blocks and 3 Con-ReLu blocks, while the scores map reconstruction phase consists of 2 deconv-concat-conv blocks. The output of each layer is a three-dimensional matrix with size of h×w×d, where h and w are the length and width of the scores map, respectively, and d is the feature dimension. a×a indicates the matrix size of the convolution kernels. Conv: convolution, Relu: rectified linear units, Pool: pooling, Deconv: deconvolution.