Purpose: We tackle the problem of online surgical phase recognition in laparoscopic procedures, which is key in developing context-aware supporting systems. We propose a novel approach to take temporal context in surgical videos into account by precise modeling of temporal neighborhoods.
Methods: We propose a two-stage model to perform phase recognition. A CNN model is used as a feature extractor to project RGB frames into a high-dimensional feature space. We introduce a novel paradigm for surgical phase recognition which utilizes graph neural networks to incorporate temporal information. Unlike recurrent neural networks and temporal convolution networks, our graph-based approach offers a more generic and flexible way for modeling temporal relationships. Each frame is a node in the graph, and the edges in the graph are used to define temporal connections among the nodes. The flexible configuration of temporal neighborhood comes at the price of losing temporal order. To mitigate this, our approach takes temporal orders into account by encoding frame positions, which is important to reliably predict surgical phases.
Results: Experiments are carried out on the public Cholec80 dataset that contains 80 annotated videos. The experimental results highlight the superior performance of the proposed approach compared to the state-of-the-art models on this dataset.
Conclusion: A novel approach for formulating video-based surgical phase recognition is presented. The results indicate that temporal information can be incorporated using graph-based models, and positional encoding is important to efficiently utilize temporal information. Graph networks open possibilities to use evidence theory for uncertainty analysis in surgical phase recognition.
Keywords: Graph neural networks; Positional encoder; Surgical AI; Surgical data science; Surgical phase recognition; Workflow analysis.
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