  We present AURA-net, a convolutional neural network (CNN) for the
segmentation of phase-contrast microscopy images. AURA-net uses transfer
learning to accelerate training and Attention mechanisms to help the network
focus on relevant image features. In this way, it can be trained efficiently
with a very limited amount of annotations. Our network can thus be used to
automate the segmentation of datasets that are generally considered too small
for deep learning techniques. AURA-net also uses a loss inspired by active
contours that is well-adapted to the specificity of phase-contrast images,
further improving performance. We show that AURA-net outperforms
state-of-the-art alternatives in several small (less than 100images) datasets.
