Automatic Generation of Synthetic Colonoscopy Videos for Domain Randomization
Abstract: An increasing number of colonoscopic guidance and assistance systems rely on machine learning algorithms which require a large amount of high-quality training data. In order to ensure high performance, the latter has to resemble a substantial portion of possible configurations. This particularly addresses varying anatomy, mucosa appearance and image sensor characteristics which are likely deteriorated by motion blur and inadequate illumination. The limited amount of readily available training data hampers to account for all of these possible configurations which results in reduced generalization capabilities of machine learning models. We propose an exemplary solution for synthesizing colonoscopy videos with substantial appearance and anatomical variations which enables to learn discriminative domain-randomized representations of the interior colon while mimicking real-world settings.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Automatic Generation of Synthetic Colonoscopy Videos for Domain Randomization ; volume:8 ; number:1 ; year:2022 ; pages:121-124 ; extent:4
Current directions in biomedical engineering ; 8, Heft 1 (2022), 121-124 (gesamt 4)
- Urheber
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Dinkar Jagtap, Abhishek
Heinrich, Mattias
Himstedt, Marian
- DOI
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10.1515/cdbme-2022-0031
- URN
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urn:nbn:de:101:1-2022080214042220019535
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:28 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Dinkar Jagtap, Abhishek
- Heinrich, Mattias
- Himstedt, Marian
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