Deep convolutional neural networks for automated segmentation of brain metastases trained on clinical data

摘要

Architecture of the trained U-Net. All convolutions used filters with a kernel size of 3. Before all convolutions, instance normalization and the activation function (leaky ReLU) were applied to the input. The residual block contained two such convolutions. Downsampling in the encoding layer was realized using a convolution with a stride of 2. In the output layers the sigmoidal function is applied to the DCNN’s output. For the moU-Net, two intermediate output layers are added (dashed red lines). The original contour data is then used to compute the cost function