Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis. So far many different works for the internal analysis have been proposed with the combination of a frame-level and an additional temporal model to address the temporal ambiguities between different workflow phases. For the External recognition task, Clip-level methods are in the focus of researchers targeting the local ambiguities present in the OR scene. In this work we evaluate combinations of different model architectures for the task of surgical workflow recognition to provide a fair comparison of the methods for both Internal and External analysis. We show that methods designed for the Internal analysis can be transferred to the external task with comparable performance gains for different architectures.
翻译:分析外科外科外科工作流程识别是一个持续的研究领域,可以分为腹腔镜(内部)和手术室(外部)分析。到目前为止,已经提出了许多不同的内部分析工作,同时结合了框架层面和额外的时间模型,以解决不同工作流程阶段之间的时间模糊性。关于外部识别任务,剪贴层的方法是研究人员针对手术场景中存在的当地模糊性进行研究的重点。在这项工作中,我们评估了外科工作流程识别任务不同模型结构的组合,以便对内部和外部分析方法进行公平的比较。我们表明,为内部分析设计的方法可以转移到外部任务,不同结构的性能收益相似。