  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.
