Deep-learning based reconstruction of the stomach from monoscopic video data
| Autor: | Hackner Ralf, Raithel Martin, Lehmann Edgar, Wittenberg Thomas |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Current Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 44-47 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2364-5504 2020-3012 |
| DOI: | 10.1515/cdbme-2020-3012 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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