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 o...

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Auteurs principaux:	Hackner Ralf, Raithel Martin, Lehmann Edgar, Wittenberg Thomas
Format:	article
Langue:	EN
Publié:	De Gruyter 2020
Sujets:	endoscopy 3d-reconstruction deep neural networks panoramic imaging Medicine R
Accès en ligne:	https://doaj.org/article/a5a819ad6f4646938a50848df5318151
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id	oai:doaj.org-article:a5a819ad6f4646938a50848df5318151
record_format	dspace
spelling	oai:doaj.org-article:a5a819ad6f4646938a50848df53181512021-12-05T14:10:42ZDeep-learning based reconstruction of the stomach from monoscopic video data2364-550410.1515/cdbme-2020-3012https://doaj.org/article/a5a819ad6f4646938a50848df53181512020-09-01T00:00:00Zhttps://doi.org/10.1515/cdbme-2020-3012https://doaj.org/toc/2364-5504For 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.Hackner RalfRaithel MartinLehmann EdgarWittenberg ThomasDe Gruyterarticleendoscopy3d-reconstructiondeep neural networkspanoramic imagingMedicineRENCurrent Directions in Biomedical Engineering, Vol 6, Iss 3, Pp 44-47 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	endoscopy 3d-reconstruction deep neural networks panoramic imaging Medicine R
spellingShingle	endoscopy 3d-reconstruction deep neural networks panoramic imaging Medicine R Hackner Ralf Raithel Martin Lehmann Edgar Wittenberg Thomas Deep-learning based reconstruction of the stomach from monoscopic video data
description	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.
format	article
author	Hackner Ralf Raithel Martin Lehmann Edgar Wittenberg Thomas
author_facet	Hackner Ralf Raithel Martin Lehmann Edgar Wittenberg Thomas
author_sort	Hackner Ralf
title	Deep-learning based reconstruction of the stomach from monoscopic video data
title_short	Deep-learning based reconstruction of the stomach from monoscopic video data
title_full	Deep-learning based reconstruction of the stomach from monoscopic video data
title_fullStr	Deep-learning based reconstruction of the stomach from monoscopic video data
title_full_unstemmed	Deep-learning based reconstruction of the stomach from monoscopic video data
title_sort	deep-learning based reconstruction of the stomach from monoscopic video data
publisher	De Gruyter
publishDate	2020
url	https://doaj.org/article/a5a819ad6f4646938a50848df5318151
work_keys_str_mv	AT hacknerralf deeplearningbasedreconstructionofthestomachfrommonoscopicvideodata AT raithelmartin deeplearningbasedreconstructionofthestomachfrommonoscopicvideodata AT lehmannedgar deeplearningbasedreconstructionofthestomachfrommonoscopicvideodata AT wittenbergthomas deeplearningbasedreconstructionofthestomachfrommonoscopicvideodata
_version_	1718371800326340608

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

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