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

• URL: http://arxiv.org/abs/2003.04949v2
• Date: Thu, 13 Aug 2020 21:24:33 GMT
• Title: LC-GAN: Image-to-image Translation Based on Generative Adversarial Network for Endoscopic Images
• Authors: Shan Lin, Fangbo Qin, Yangming Li, Randall A. Bly, Kris S. Moe, Blake Hannaford
• Abstract summary: We propose an image-to-image translation model live-cadaver GAN (LC-GAN) based on generative adversarial networks (GANs) For live image segmentation, we first translate the live images to fake-cadaveric images with LC-GAN and then perform segmentation on the fake-cadaveric images with models trained on the real cadaveric dataset. Our model achieves better image-to-image translation and leads to improved segmentation performance in the proposed cross-domain segmentation task.
• Score: 22.253074722129053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent vision is appealing in computer-assisted and robotic surgeries. Vision-based analysis with deep learning usually requires large labeled datasets, but manual data labeling is expensive and time-consuming in medical problems. We investigate a novel cross-domain strategy to reduce the need for manual data labeling by proposing an image-to-image translation model live-cadaver GAN (LC-GAN) based on generative adversarial networks (GANs). We consider a situation when a labeled cadaveric surgery dataset is available while 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 to fake-cadaveric images with LC-GAN and then perform segmentation on the fake-cadaveric images with models trained on the real cadaveric dataset. The proposed method fully makes use of the labeled cadaveric dataset for live image segmentation without the need to label the live dataset. LC-GAN has two generators with different architectures that leverage the deep feature representation learned from the cadaveric image based segmentation task. Moreover, we propose the structural similarity loss and segmentation consistency loss to improve the semantic consistency during translation. Our model achieves better image-to-image translation and leads to improved segmentation performance in the proposed cross-domain segmentation task.

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