  Gastric endoscopy is a standard clinical process that enables medical
practitioners to diagnose various lesions inside a patient's stomach. If any
lesion is found, it is very important to perceive the location of the lesion
relative to the global view of the stomach. Our previous research showed that
this could be addressed by reconstructing the whole stomach shape from
chromoendoscopic images using a structure-from-motion (SfM) pipeline, in which
indigo carmine (IC) blue dye sprayed images were used to increase feature
matches for SfM by enhancing stomach surface's textures. However, spraying the
IC dye to the whole stomach requires additional time, labor, and cost, which is
not desirable for patients and practitioners. In this paper, we propose an
alternative way to achieve whole stomach 3D reconstruction without the need of
the IC dye by generating virtual IC-sprayed (VIC) images based on
image-to-image style translation trained on unpaired real no-IC and IC-sprayed
images. We have specifically investigated the effect of input and output color
channel selection for generating the VIC images and found that translating
no-IC green-channel images to IC-sprayed red-channel images gives the best SfM
reconstruction result.
