Vision-Based Neurosurgical Guidance: Unsupervised Localization and Camera-Pose Prediction
This addresses localization challenges in endoscopic neurosurgery, offering a tool for guidance, but it is incremental as it builds on existing deep learning and YOLOv7 methods.
The paper tackles the problem of localizing endoscopic devices during surgery by developing an unsupervised deep learning method that constructs a surgical path from videos to map frames and estimate viewing angles, tested on surgical and synthetic datasets with an online tool provided for researchers.
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.