2.5D mapping of the esophagus as imaging quality and completeness assuring extension for endoscopic computer-aided detection systems

Quality and completeness of images taken during endoscopy of the esophagus are of high importance for their use in computer-aided detection (CADe) systems. Providing a tissue map to show visited locations with sufficient image quality, and providing feedback on the completeness of the procedure could ensure standards are met for input to CADe systems. However, existing solutions are either not translatable to esophageal endoscopy procedures or impose unrealistic restrictions on endoscope movement. Our study shows the first steps in developing a tissue mapping algorithm for esophageal endoscopy, that is robust against camera rotation and movement natural to endoscopy procedures. The method combines SIFT-feature-based visual odometry and image stitching to generate a 2.5D map of the esophagus. The algorithm is tested in a virtual model environment, on 6 scenarios mimicking the movements and rotations of an endoscope during a pullback procedure. Results are evaluated based on ground-truth camera pose and distortion of the generated map. It is demonstrated that the proposed algorithm can generate accurate tissue maps but is sensitive to errors in estimated camera position and rotation.