Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy

INIMALLY invasiveprocedures in the head and neck, e. g. functional endoscopic sinus surgery, typically employ surgical navigation systems to provide surgeons with additional anatomical and positional information. This helps them avoid critical structures, such as the brain, eyes, and major arteries, that are spatially close to the sinus cavities and must not be disturbed during surgery. Computer vision-based navigation systems that rely on the intra-operative endoscopic video stream and do not introduce additional hardware are both easy to integrate into clinical workflow and cost-effective. Such systems generally require registration of preoperative data, such as CT scans or statistical models, to the intraoperative videodata [1], [2], [3], [4]. This registration must be highly accurate in order to guarantee reliable performance of the navigation system. To enable an accurate registration, a feature-based video-CT registration algorithm requires accurate andsufficiently dense intra-operative 3D reconstructions of the anatomy from endoscopic videos.