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arXiv · 1807.10565

DeepPhase: Surgical Phase Recognition in CATARACTS Videos

Odysseas Zisimopoulos·Evangello Flouty·Imanol Luengo·Petros Giataganas·Jean Nehme·Andre Chow·Danail Stoyanov
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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.

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Odysseas Zisimopoulos, Evangello Flouty, Imanol Luengo, Petros Giataganas, Jean Nehme, Andre Chow, Danail Stoyanov. 2018-07-17. DeepPhase: Surgical Phase Recognition in CATARACTS Videos. https://arxiv.org/abs/1807.10565

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