Automated Segmentation of Epithelial Tissue Using Cycle-Consistent Generative Adversarial Networks

Haering, M.; Grosshans, J.; Wolf, F.; Eule, S.

doi:10.1101/311373

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bioRxiv · 10.1101/311373

Automated Segmentation of Epithelial Tissue Using Cycle-Consistent Generative Adversarial Networks

Haering, M.·Grosshans, J.·Wolf, F.·Eule, S.

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Abstract

A central problem in biomedical imaging is the automated segmentation of images for further quantitative analysis. Recently, fully convolutional neural networks, such as the U-Net, were applied successfully in a variety of segmentation tasks. A downside of this approach is the requirement for a large amount of well-prepared training samples, consisting of image - ground truth mask pairs. Since training data must be created by hand for each experiment, this task can be very costly and time-consuming. Here, we present a segmentation method based on cycle consistent generative adversarial networks, which can be trained even in absence of prepared image - mask pairs. We show that it successfully performs image segmentation tasks on samples with substantial defects and even generalizes well to different tissue types.

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Haering, M., Grosshans, J., Wolf, F., Eule, S.. 2018-04-30. Automated Segmentation of Epithelial Tissue Using Cycle-Consistent Generative Adversarial Networks. https://doi.org/10.1101/311373

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