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.
@article{arxiv.2102.01389,
title = {aura-net : robust segmentation of phase-contrast microscopy images with few annotations},
author = {Ethan Cohen and Virginie Uhlmann},
journal= {arXiv preprint arXiv:2102.01389},
year = {2021}
}