# 120 — Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach

The algorithm is a progression of preprocessing that leads to two convolutional 3D neural networks: First, the image is subtracted from its mean and divided from its variance in order to globally normalize the images between scans. Then the image is divided into small 3D patches (11vox³). These patches are used to train CNN1, the output of which is used to train CNN2, which, which uses $FC$ layers to return the voxelwise probability of belonging to a positive “lesion” or negative “non-lesion” class.