  Localizing oneself during endoscopic procedures can be problematic due to the
lack of distinguishable textures and landmarks, as well as difficulties due to
the endoscopic device such as a limited field of view and challenging lighting
conditions. Expert knowledge shaped by years of experience is required for
localization within the human body during endoscopic procedures. In this work,
we present a deep learning method based on anatomy recognition, that constructs
a surgical path in an unsupervised manner from surgical videos, modelling
relative location and variations due to different viewing angles. At inference
time, the model can map an unseen video's frames on the path and estimate the
viewing angle, aiming to provide guidance, for instance, to reach a particular
destination. We test the method on a dataset consisting of surgical videos of
transsphenoidal adenomectomies, as well as on a synthetic dataset. An online
tool that lets researchers upload their surgical videos to obtain anatomy
detections and the weights of the trained YOLOv7 model are available at:
https://surgicalvision.bmic.ethz.ch.
