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

Ethan Cohen; Virginie Uhlmann

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arXiv · 2102.01389

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

Ethan Cohen·Virginie Uhlmann

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Abstract

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.

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eess.IV cs.CV cs.LG

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BibTeX RIS

Ethan Cohen, Virginie Uhlmann. 2021-02-02. aura-net : robust segmentation of phase-contrast microscopy images with few annotations. https://arxiv.org/abs/2102.01389

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