  In the medical domain, the lack of large training data sets and benchmarks is
often a limiting factor for training deep neural networks. In contrast to
expensive manual labeling, computer simulations can generate large and fully
labeled data sets with a minimum of manual effort. However, models that are
trained on simulated data usually do not translate well to real scenarios. To
bridge the domain gap between simulated and real laparoscopic images, we
exploit recent advances in unpaired image-to-image translation. We extent an
image-to-image translation method to generate a diverse multitude of
realistically looking synthetic images based on images from a simple
laparoscopy simulation. By incorporating means to ensure that the image content
is preserved during the translation process, we ensure that the labels given
for the simulated images remain valid for their realistically looking
translations. This way, we are able to generate a large, fully labeled
synthetic data set of laparoscopic images with realistic appearance. We show
that this data set can be used to train models for the task of liver
segmentation of laparoscopic images. We achieve average dice scores of up to
0.89 in some patients without manually labeling a single laparoscopic image and
show that using our synthetic data to pre-train models can greatly improve
their performance. The synthetic data set will be made publicly available,
fully labeled with segmentation maps, depth maps, normal maps, and positions of
tools and camera (http://opencas.dkfz.de/image2image).
