Colorectal cancer is one of the leading causes of cancer-related deaths worldwide, although it can be effectively treated if detected early. Teleoperated flexible endoscopes are an emerging technology to promote participation in these preventive screenings. Real-time pose estimation is therefore essential to enable feedback to the robotic endoscope's control system. Vision-based endoscope localization approaches are a promising avenue, since they do not require extra sensors on board the endoscopes. In this work, we compare several state-of-the-art algorithms for computing the image motion (optical flow), which is then used with a supervised learning strategy to provide an accurate estimate of the 6 degree of freedom endoscope motion. The method is validated using a robotically actuated endoscope in a human colon simulator, and represents a preliminary effort towards testing with clinical video data.
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