To enable context-aware computer assistance in the operating room of the future, cognitive systems need to understand automatically which surgical phase is being performed by the medical team. The primary source of information for surgical phase recognition is typically video, which presents two challenges: extracting meaningful features from the video stream and effectively modeling temporal information in the sequence of visual features. For temporal modeling, attention mechanisms have gained popularity due to their ability to capture long-range dependencies. In this paper, we explore design choices for attention in existing temporal models for surgical phase recognition and propose a novel approach that uses attention more effectively and does not require hand-crafted constraints: TUNeS, an efficient and simple temporal model that incorporates self-attention at the core of a convolutional U-Net structure. In addition, we propose to train the feature extractor, a standard CNN, together with an LSTM on preferably long video segments, i.e., with long temporal context. In our experiments, almost all temporal models performed better on top of feature extractors that were trained with longer temporal context. On these contextualized features, TUNeS achieves state-of-the-art results on the Cholec80 dataset. This study offers new insights on how to use attention mechanisms to build accurate and efficient temporal models for surgical phase recognition. Implementing automatic surgical phase recognition is essential to automate the analysis and optimization of surgical workflows and to enable context-aware computer assistance during surgery, thus ultimately improving patient care.

翻译：为了在未来手术室中实现情境感知的计算机辅助，认知系统需要自动理解医疗团队正在执行的手术阶段。手术阶段识别的主要信息源通常是视频，这带来两个挑战：从视频流中提取有意义的特征，以及有效建模视觉特征序列中的时间信息。在时间建模方面，注意力机制因其捕捉长程依赖关系的能力而日益流行。本文探讨了现有手术阶段识别时间模型中注意力的设计选择，并提出了一种更有效利用注意力且无需手工约束的新方法：TUNeS，一种高效简洁的时间模型，它将自注意力嵌入卷积U-Net结构的核心。此外，我们建议将特征提取器（标准CNN）与LSTM一起在尽可能长的视频片段（即具有长时上下文）上进行训练。在我们的实验中，几乎所有时间模型在基于更长时上下文训练的特征提取器上均表现更优。基于这些上下文特征，TUNeS在Cholec80数据集上取得了最先进的结果。本研究为如何利用注意力机制构建精确高效的手术阶段识别时间模型提供了新见解。实现自动手术阶段识别对于自动分析和优化手术流程、实现术中情境感知的计算机辅助至关重要，从而最终改善患者护理。