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Intelligent vision is appealing in computer-assisted and robotic surgeries.Vision-based analysis with deep learning usually requires large labeleddatasets, but manual data labeling is expensive and time-consuming in medicalproblems. We investigate a novel cross-domain strategy to reduce the need formanual data labeling by proposing an image-to-image translation modellive-cadaver GAN (LC-GAN) based on generative adversarial networks (GANs). Weconsider a situation when a labeled cadaveric surgery dataset is availablewhile the task is instrument segmentation on an unlabeled live surgery dataset.We train LC-GAN to learn the mappings between the cadaveric and live images.For live image segmentation, we first translate the live images tofake-cadaveric images with LC-GAN and then perform segmentation on thefake-cadaveric images with models trained on the real cadaveric dataset. Theproposed method fully makes use of the labeled cadaveric dataset for live imagesegmentation without the need to label the live dataset. LC-GAN has twogenerators with different architectures that leverage the deep featurerepresentation learned from the cadaveric image based segmentation task.Moreover, we propose the structural similarity loss and segmentationconsistency loss to improve the semantic consistency during translation. Ourmodel achieves better image-to-image translation and leads to improvedsegmentation performance in the proposed cross-domain segmentation task.

LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images