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. Computerassisted 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.
Moreover, the deep learning scheme has been adopted to detect surgical instruments and surgical phases in cataract videos, which can assist surgeons in standardizing procedures REF .
DeepPhase: Surgical Phase Recognition in CATARACTS Videos
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Computer Science Mathematics