  We present the first application of deep neural networks to the semantic
segmentation of cosmological filaments and walls in the Large Scale Structure
of the Universe. Our results are based on a deep Convolutional Neural Network
(CNN) with a U-Net architecture trained using an existing state-of-the-art
manually-guided segmentation method. We successfully trained an tested an U-Net
with a Voronoi model and an N-body simulation. The predicted segmentation masks
from the Voronoi model have a Dice coefficient of 0.95 and 0.97 for filaments
and mask respectively. The predicted segmentation masks from the N-body
simulation have a Dice coefficient of 0.78 and 0.72 for walls and filaments
respectively. The relatively lower Dice coefficient in the filament mask is the
result of filaments that were predicted by the U-Net model but were not present
in the original segmentation mask.
  Our results show that for a well-defined dataset such as the Voronoi model
the U-Net has excellent performance. In the case of the N-body dataset the
U-Net produced a filament mask of higher quality than the segmentation mask
obtained from a state-of-the art method. The U-Net performs better than the
method used to train it, being able to find even the tenuous filaments that the
manually-guided segmentation failed to identify.
  The U-Net presented here can process a $512^3$ volume in a few minutes and
without the need of complex pre-processing. Deep CNN have great potential as an
efficient and accurate analysis tool for the next generation large-volume
computer N-body simulations and galaxy surveys.
