  Prostate cancer is one of the most prevalent cancers worldwide. One of the
key factors in reducing its mortality is based on early detection. The
computer-aided diagnosis systems for this task are based on the glandular
structural analysis in histology images. Hence, accurate gland detection and
segmentation is crucial for a successful prediction. The methodological basis
of this work is a prostate gland segmentation based on U-Net convolutional
neural network architectures modified with residual and multi-resolution
blocks, trained using data augmentation techniques. The residual configuration
outperforms in the test subset the previous state-of-the-art approaches in an
image-level comparison, reaching an average Dice Index of 0.77.
