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DeepPhase: Surgical Phase Recognition in CATARACTS Videos

Zisimopoulos, O; Flouty, E; Luengo, I; Giataganas, P; Nehme, J; Chow, A; Stoyanov, D; (2018) DeepPhase: Surgical Phase Recognition in CATARACTS Videos. In: Frangi, A and Schnabel, J and Davatzikos, C and Alberola-López, C and Fichtinger, G, (eds.) Medical Image Computing and Computer Assisted Intervention (MICCAI 2018): 21st International Conference, Proceedings, Part IV. (pp. pp. 265-272). Springer: Cham, Switzerland. Green open access

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Abstract

Automated surgical workflow analysis and understanding can assist surgeons to standardize procedures and enhance post-surgical assessment and indexing, as well as, interventional monitoring. Computer-assisted interventional (CAI) systems based on video can perform workflow estimation through surgical instruments’ recognition while linking them to an ontology of procedural phases. In this work, we adopt a deep learning paradigm to detect surgical instruments in cataract surgery videos which in turn feed a surgical phase inference recurrent network that encodes temporal aspects of phase steps within the phase classification. Our models present comparable to state-of-the-art results for surgical tool detection and phase recognition with accuracies of 99 and 78% respectively.

Type: Proceedings paper
Title: DeepPhase: Surgical Phase Recognition in CATARACTS Videos
Event: MICCAI 2018, 21st International Conference, 16-20 September 2018, Granada, Spain
ISBN-13: 978-3-030-00936-6
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00937-3_31
Publisher version: https://doi.org/10.1007/978-3-030-00937-3_31
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Surgical vision, instrument detection, surgical workflow, deep learning, surgical data science
UCL classification: UCL
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
URI: https://discovery.ucl.ac.uk/id/eprint/10060022
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