AURA-NET: ROBUST SEGMENTATION OF PHASE-CONTRAST MICROSCOPY IMAGES WITH FEW ANNOTATIONS

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 100 images) datasets.

Automated summary

AURA-net is a convolutional neural network designed for segmenting phase-contrast microscopy images, utilizing transfer learning and Attention mechanisms to enhance training efficiency, suitable for small datasets. Additionally, it employs a loss function inspired by active contours to further improve performance.