Task-guided Domain Gap Reduction for Monocular Depth Prediction in
  Endoscopy
Anita Rau Binod Bhattarai Lourdes Agapito Danail Stoyanov
Colorectal cancer remains one of the deadliest cancers in the world. In
recent years computer-aided methods have aimed to enhance cancer screening and
improve the quality and availability of colonoscopies by automatizing
sub-tasks. One such task is predicting depth from monocular video frames, which
can assist endoscopic navigation. As ground truth depth from standard in-vivo
colonoscopy remains unobtainable due to hardware constraints, two approaches
have aimed to circumvent the need for real training data: supervised methods
trained on labeled synthetic data and self-supervised models trained on
unlabeled real data. However, self-supervised methods depend on unreliable loss
functions that struggle with edges, self-occlusion, and lighting inconsistency.
Methods trained on synthetic data can provide accurate depth for synthetic
geometries but do not use any geometric supervisory signal from real data and
overfit to synthetic anatomies and properties. This work proposes a novel
approach to leverage labeled synthetic and unlabeled real data. While previous
domain adaptation methods indiscriminately enforce the distributions of both
input data modalities to coincide, we focus on the end task, depth prediction,
and translate only essential information between the input domains. Our
approach results in more resilient and accurate depth maps of real colonoscopy
sequences.