Image partitioning and illumination in image-based pose detection for teleoperated flexible endoscopes

摘要

ObjectiveColorectal cancer is one of the leading causes of cancer-related deaths in the world, although it can be effectively treated if detected early. Teleoperated flexible endoscopes are an emerging technology to ease patient apprehension about the procedure, and subsequently increase compliance. Essential to teleoperation is robust feedback reflecting the change in pose (i.e., position and orientation) of the tip of the endoscope. The goal of this study is to first describe a novel image-based tracking system for teleoperated flexible endoscopes, and subsequently determine its viability in a clinical setting. The proposed approach leverages artificial neural networks (ANNs) to learn the mapping that links the optical flow between two sequential images to the change in the pose of the camera. Secondly, the study investigates for the first time how narrow band illumination (NBI) – today available in commercial gastrointestinal endoscopes – can be applied to enhance feature extraction, and quantify the effect of NBI and white light illumination (WLI), as well as their color information, on the strength of features extracted from the endoscopic camera stream.