  Haustral folds are colon wall protrusions implicated for high polyp miss rate
during optical colonoscopy procedures. If segmented accurately, haustral folds
can allow for better estimation of missed surface and can also serve as
valuable landmarks for registering pre-treatment virtual (CT) and optical
colonoscopies, to guide navigation towards the anomalies found in pre-treatment
scans. We present a novel generative adversarial network, FoldIt, for
feature-consistent image translation of optical colonoscopy videos to virtual
colonoscopy renderings with haustral fold overlays. A new transitive loss is
introduced in order to leverage ground truth information between haustral fold
annotations and virtual colonoscopy renderings. We demonstrate the
effectiveness of our model on real challenging optical colonoscopy videos as
well as on textured virtual colonoscopy videos with clinician-verified haustral
fold annotations. All code and scripts to reproduce the experiments of this
paper will be made available via our Computational Endoscopy Platform at
https://github.com/nadeemlab/CEP.
