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

Abstract

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 geometry of the underlying structure is required. Structure from motion 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 existing 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.

Video data security prevention system based on artificial intelligence

“…The video data security prevention strategy is determined from the following three angles: the confidentiality, integrity and transmission security of video data encryption 10 . When the video data is tampered with, it can be judged by the hashing algorithm 11 .…”