Analyzing surgical workflow is crucial for surgical assistance robots to understand surgeries. With the understanding of the complete surgical workflow, the robots are able to assist the surgeons in intra-operative events, such as by giving a warning when the surgeon is entering specific keys or high-risk phases. Deep learning techniques have recently been widely applied to recognizing surgical workflows. Many of the existing temporal neural network models are limited in their capability to handle long-term dependencies in the data, instead, relying upon the strong performance of the underlying per-frame visual models. We propose a new temporal network structure that leverages task-specific network representation to collect long-term sufficient statistics that are propagated by a sufficient statistics model (SSM). We implement our approach within an LSTM backbone for the task of surgical phase recognition and explore several choices for propagated statistics. We demonstrate superior results over existing and novel state-of-the-art segmentation techniques on two laparoscopic cholecystectomy datasets: the publicly available Cholec80 dataset and MGH100, a novel dataset with more challenging and clinically meaningful segment labels.

翻译：分析外科手术工作流程对于外科协助机器人了解外科手术至关重要。由于对全外科工作流程的理解,机器人能够在手术期间协助外科医生,例如当外科医生进入特定关键或高风险阶段时发出警告。最近,深入的学习技术被广泛用于认识外科工作流程。许多现有的时间神经网络模型在处理数据长期依赖性的能力方面受到限制,相反,依靠每框架直观模型的强大性能。我们提议一个新的时间网络结构,利用特定任务网络代表来收集长期足够的统计数据,这些数据通过足够的统计模型(SSSSM)传播。我们在LSTM主干线内实施我们的方法,以进行外科阶段识别,并探索几种传播统计数据的选择。我们展示了优异于目前和新颖的关于两个大肠杆细胞切除元数据集的分解技术的结果:即公开可用的Cholec80数据集和MGH100,这是一个具有更具有挑战性和临床意义的新数据集。