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arXiv · 2306.02901

A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis

Aline Sindel·Andreas Maier·Vincent Christlein
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Abstract

Unpaired image-to-image translation of retinal images can efficiently increase the training dataset for deep-learning-based multi-modal retinal registration methods. Our method integrates a vessel segmentation network into the image-to-image translation task by extending the CycleGAN framework. The segmentation network is inserted prior to a UNet vision transformer generator network and serves as a shared representation between both domains. We reformulate the original identity loss to learn the direct mapping between the vessel segmentation and the real image. Additionally, we add a segmentation loss term to ensure shared vessel locations between fake and real images. In the experiments, our method shows a visually realistic look and preserves the vessel structures, which is a prerequisite for generating multi-modal training data for image registration.

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Aline Sindel, Andreas Maier, Vincent Christlein. 2023-06-05. A Vessel-Segmentation-Based CycleGAN for Unpaired Multi-modal Retinal Image Synthesis. https://doi.org/10.1007/978-3-658-41657-7_11

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