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

aura-net : robust segmentation of phase-contrast microscopy images with few annotations