  This article presents a convolutional neural network for the automatic
segmentation of brain tumors in multimodal 3D MR images based on a U-net
architecture.We evaluate the use of a densely connected convolutional network
encoder (DenseNet) which was pretrained on the ImageNet data set. We detail two
network architectures that can take into account multiple 3D images as inputs.
This work aims to identify if a generic pretrained network can be used for very
specific medical applications where the target data differ both in the number
of spatial dimensions as well as in the number of inputs channels. Moreover in
order to regularize this transfer learning task we only train the decoder part
of the U-net architecture. We evaluate the effectiveness of the proposed
approach on the BRATS 2018 segmentation challenge where we obtained dice scores
of 0.79, 0.90, 0.85 and 95/% Hausdorff distance of 2.9mm, 3.95mm, and 6.48mm
for enhanced tumor core, whole tumor and tumor core respectively on the
validation set. This scores degrades to 0.77, 0.88, 0.78 and 95 /% Hausdorff
distance of 3.6mm, 5.72mm, and 5.83mm on the testing set.
