  Atrial Fibrillation (AF) is a common electro-physiological cardiac disorder
that causes changes in the anatomy of the atria. A better characterization of
these changes is desirable for the definition of clinical biomarkers,
furthermore, thus there is a need for its fully automatic segmentation from
clinical images. In this work, we present an architecture based on
3D-convolution kernels, a Volumetric Fully Convolution Neural Network (V-FCNN),
able to segment the entire volume in a one-shot, and consequently integrate the
implicit spatial redundancy present in high-resolution images. A loss function
based on the mixture of both Mean Square Error (MSE) and Dice Loss (DL) is
used, in an attempt to combine the ability to capture the bulk shape as well as
the reduction of local errors products by over-segmentation. Results
demonstrate a reasonable performance in the middle region of the atria along
with the impact of the challenges of capturing the variability of the pulmonary
veins or the identification of the valve plane that separates the atria to the
ventricle. A final dice of $92.5\%$ in $54$ patients ($4752$ atria test slices
in total) is shown.
