Assisted phase and step annotation for surgical videos.

Purpose: Annotation of surgical videos is a time-consuming task which requires specific knowledge. In this paper, we present and evaluate a deep learning-based method that includes pre-annotation of the phases and steps in surgical videos and user assistance in the annotation process.

Methods: We propose a classification function that automatically detects errors and infers temporal coherence in predictions made by a convolutional neural network.