Fig. 1

Architecture of the externally developed and trained 3D CNN with a fully convolutional encoder–decoder architecture with 3D convolutions, residual-block connections and four reductions of the feature map size. The two input images (T1-weighted post-contrast patch and registered FLAIR patch) were fed into the same encoder path with shared weights. Following every residual-block, the feature maps for the T1-weighted and the FLAIR input were concatenated and fed into the decoder. A segmentation mask was predicted, indicating contrast-enhancing lesions and background classes (grey matter, white matter, cerebrospinal fluid and FLAIR lesions). CNN, convolutional neural network; FLAIR, fluid-attenuated inversion recovery