Deep-learning based reconstruction of the stomach from monoscopic video data

For the gastroscopic examination of the stomach, the restricted field of view related to the ""keyhole""-perspective of the endoscope is known to be a visual limitation. Thus, a panoramic extension can enlarge the field of vision, supports the endoscopist during the examination, and ensures that all of the inner stomach walls are visually inspected. To compute such a panorama of the stomach, knowledge about the geom-etry of the underlying structure is required. Structure from mo-tion an approach to reconstruct the necessary information about the 3D-structure from monocular image sequences as provided by a gastroscope. We examine and evaluate an exist-ing deep neuronal network for stereo reconstruction, in order to approximate the geometry of stomach parts from a set of consecutive acquired image pairs from gastroscopic videos.