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Dense Depth Estimation in Monocular Endoscopy with Self-supervised Learning Methods

We present a self-supervised approach to training convolutional neural networks for dense depth estimation from monocular endoscopy data without a priori modeling of anatomy or shading. Our method only requires monocular endoscopic videos and a multi-view stereo method, e. g., structure from motion,...

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Detalhes bibliográficos
Publicado no:IEEE Trans Med Imaging
Main Authors: Liu, Xingtong, Sinha, Ayushi, Ishii, Masaru, Hager, Gregory D., Reiter, Austin, Taylor, Russell H., Unberath, Mathias
Formato: Artigo
Idioma:Inglês
Publicado em: 2019
Assuntos:
Acesso em linha:https://ncbi.nlm.nih.gov/pmc/articles/PMC7289272/
https://ncbi.nlm.nih.gov/pubmed/31689184
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1109/TMI.2019.2950936
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