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 .