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

• URL: http://arxiv.org/abs/2102.01389v1
• Date: Tue, 2 Feb 2021 08:47:14 GMT
• Title: aura-net : robust segmentation of phase-contrast microscopy images with few annotations
• Authors: Ethan Cohen and Virginie Uhlmann
• Abstract summary: AURA-net is a convolutional neural network (CNN) for the segmentation of phase-contrast microscopy images. We show that AURA-net outperforms state-of-the-art alternatives in several small (less than 100images) datasets.
• Score: 2.1574781022415364
• License: http://creativecommons.org/licenses/by/4.0/
• 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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