  Optical colonoscopy (OC), the most prevalent colon cancer screening tool, has
a high miss rate due to a number of factors, including the geometry of the
colon (haustral fold and sharp bends occlusions), endoscopist inexperience or
fatigue, endoscope field of view, etc. We present a framework to visualize the
missed regions per-frame during the colonoscopy, and provides a workable
clinical solution. Specifically, we make use of 3D reconstructed virtual
colonoscopy (VC) data and the insight that VC and OC share the same underlying
geometry but differ in color, texture and specular reflections, embedded in the
OC domain. A lossy unpaired image-to-image translation model is introduced with
enforced shared latent space for OC and VC. This shared latent space captures
the geometric information while deferring the color, texture, and specular
information creation to additional Gaussian noise input. This additional noise
input can be utilized to generate one-to-many mappings from VC to OC and OC to
OC. The code, data and trained models will be released via our Computational
Endoscopy Platform at https://github.com/nadeemlab/CEP.
