Purpose: Annotation of surgical activities becomes increasingly important for many recent applications such as surgical workflow analysis, surgical situation awareness, and the design of the operating room of the future, especially to train machine learning methods in order to develop intelligent assistance. Currently, annotation is mostly performed by observers with medical background and is incredibly costly and time-consuming, creating a major bottleneck for the above-mentioned technologies. In this paper, we propose a way to eliminate, or at least limit, the human intervention in the annotation process.
Methods: Meaningful information about interaction between objects is inherently available in virtual reality environments. We propose a strategy to convert automatically this information into annotations in order to provide as output individual surgical process models.
Validation: We implemented our approach through a peg-transfer task simulator and compared it to manual annotations. To assess the impact of our contribution, we studied both intra- and inter-observer variability.
Results and conclusion: In average, manual annotations took more than 12 min for 1 min of video to achieve low-level physical activity annotation, whereas automatic annotation is achieved in less than a second for the same video period. We also demonstrated that manual annotation introduced mistakes as well as intra- and inter-observer variability that our method is able to suppress due to the high precision and reproducibility.
Keywords: Automatic annotation; Surgical process model; Surgical simulation.