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

Hackner R, Raithel M, Lehmann E, Wittenberg T (2020)

Publication Language: English

Publication Type: Journal article, Original article

Publication year: 2020

Journal

Current Directions in Biomedical Engineering De Gruyter

Book Volume: 6

Journal Issue: 3

URI: https://www.degruyter.com/document/doi/10.1515/cdbme-2020-3012/html

DOI: 10.1515/cdbme-2020-3012

Open Access Link: https://www.degruyter.com/document/doi/10.1515/cdbme-2020-3012/html

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 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.

Authors with CRIS profile

Ralf Hackner Technische Fakultät Thomas Wittenberg Technische Fakultät

Involved external institutions

Waldkrankenhaus St Marien DE Germany (DE) E&L medical systems GmbH DE Germany (DE)

How to cite

APA:

Hackner, R., Raithel, M., Lehmann, E., & Wittenberg, T. (2020). Deep-learning based reconstruction of the stomach from monoscopic video data. Current Directions in Biomedical Engineering, 6(3). https://doi.org/10.1515/cdbme-2020-3012

MLA:

Hackner, Ralf, et al. "Deep-learning based reconstruction of the stomach from monoscopic video data." Current Directions in Biomedical Engineering 6.3 (2020).

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