  We present the first attempt to perform short glass fiber semantic
segmentation from X-ray computed tomography volumetric datasets at medium (3.9
{\mu}m isotropic) and low (8.3 {\mu}m isotropic) resolution using deep learning
architectures. We performed experiments on both synthetic and real CT scans and
evaluated deep fully convolutional architectures with both 2D and 3D kernels.
Our artificial neural networks outperform existing methods at both medium and
low resolution scans.
