  An increasing number of colonoscopic guidance and assistance systems rely on
machine learning algorithms which require a large amount of high-quality
training data. In order to ensure high performance, the latter has to resemble
a substantial portion of possible configurations. This particularly addresses
varying anatomy, mucosa appearance and image sensor characteristics which are
likely deteriorated by motion blur and inadequate illumination. The limited
amount of readily available training data hampers to account for all of these
possible configurations which results in reduced generalization capabilities of
machine learning models. We propose an exemplary solution for synthesizing
colonoscopy videos with substantial appearance and anatomical variations which
enables to learn discriminative domain-randomized representations of the
interior colon while mimicking real-world settings.
