Figure 1.

Network structure: (a) The overall U-net structure with two convolution block options: (b) Plain Connected Block in PlainUnet or (c) Densely Connected Block in DenseUnet. In DenseUnet, the filter number (growth-rate) k=48; in the PlainUnet setting, the filter number starts at f=64 and doubles after pooling. Stem input is a single 3×3 convolutional layer with 2k or f filters. Normalizations are applied before the Exponential Linear Units (ELU) and 3×3 convolutional layers. 2×2 average pooling is used in the Transition Down, and a transposed-convolutional layer with 2×2 kernel and stride size of 2 is applied in the Transition Up. A 1×1 convolutional layer and softmax activation output the predictions for 11 classes (background + 10 organs). At each resolution, the auxiliary prediction is used for deeply supervised training.