SUrgical PRediction GAN for Events Anticipation

• URL: http://arxiv.org/abs/2105.04642v1
• Date: Mon, 10 May 2021 19:56:45 GMT
• Title: SUrgical PRediction GAN for Events Anticipation
• Authors: Yutong Ban and Guy Rosman and Thomas Ward and Daniel Hashimoto and Taisei Kondo and Hidekazu Iwaki and Ozanan Meireles and Daniela Rus
• Abstract summary: We used a novel GAN formulation that sampled the future surgical phases trajectory conditioned, on past laparoscopic video frames. We demonstrated its effectiveness in inferring and predicting the progress of laparoscopic cholecystectomy videos. We surveyed surgeons to evaluate the plausibility of these predicted trajectories.
• Score: 38.65189355224683
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Comprehension of surgical workflow is the foundation upon which computers build the understanding of surgery. In this work, we moved beyond just the identification of surgical phases to predict future surgical phases and the transitions between them. We used a novel GAN formulation that sampled the future surgical phases trajectory conditioned, on past laparoscopic video frames, and compared it to state-of-the-art approaches for surgical video analysis and alternative prediction methods. We demonstrated its effectiveness in inferring and predicting the progress of laparoscopic cholecystectomy videos. We quantified the horizon-accuracy trade-off and explored average performance as well as the performance on the more difficult, and clinically important, transitions between phases. Lastly, we surveyed surgeons to evaluate the plausibility of these predicted trajectories.

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