  This paper proposes a realistic image generation method for visualization in
endoscopic simulation systems. Endoscopic diagnosis and treatment are performed
in many hospitals. To reduce complications related to endoscope insertions,
endoscopic simulation systems are used for training or rehearsal of endoscope
insertions. However, current simulation systems generate non-realistic virtual
endoscopic images. To improve the value of the simulation systems, improvement
of reality of their generated images is necessary. We propose a realistic image
generation method for endoscopic simulation systems. Virtual endoscopic images
are generated by using a volume rendering method from a CT volume of a patient.
We improve the reality of the virtual endoscopic images using a virtual-to-real
image-domain translation technique. The image-domain translator is implemented
as a fully convolutional network (FCN). We train the FCN by minimizing a cycle
consistency loss function. The FCN is trained using unpaired virtual and real
endoscopic images. To obtain high quality image-domain translation results, we
perform an image cleansing to the real endoscopic image set. We tested to use
the shallow U-Net, U-Net, deep U-Net, and U-Net having residual units as the
image-domain translator. The deep U-Net and U-Net having residual units
generated quite realistic images.
