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FetNet: A Recurrent Convolutional Network for Occlusion Identification in Fetoscopic Videos

Bano, S; Vasconcelos, F; Vander Poorten, E; Vercauteren, T; Ourselin, S; Deprest, J; Stoyanov, D; (2020) FetNet: A Recurrent Convolutional Network for Occlusion Identification in Fetoscopic Videos. International Journal of Computer Assisted Radiology and Surgery 10.1007/s11548-020-02169-0. (In press). Green open access

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Type: Article
Title: FetNet: A Recurrent Convolutional Network for Occlusion Identification in Fetoscopic Videos
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11548-020-02169-0
Publisher version: https://doi.org/10.1007/s11548-020-02169-0
Language: English
Additional information: © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Deep learning, Surgical vision, Twin-to-twin transfusion syndrome (TTTS), Fetoscopy, Video segmentation, Computer assisted interventions (CAI)
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10095914
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