Purpose: We propose a formal framework for the modeling and segmentation of minimally-invasive surgical tasks using a unified set of motion primitives (MPs) to enable more objective labeling and the aggregation of different datasets. Methods: We model dry-lab surgical tasks as finite state machines, representing how the execution of MPs as the basic surgical actions results in the change of surgical context, which characterizes the physical interactions among tools and objects in the surgical environment. We develop methods for labeling surgical context based on video data and for automatic translation of context to MP labels. We then use our framework to create the COntext and Motion Primitive Aggregate Surgical Set (COMPASS), including six dry-lab surgical tasks from three publicly-available datasets (JIGSAWS, DESK, and ROSMA), with kinematic and video data and context and MP labels. Results: Our context labeling method achieves near-perfect agreement between consensus labels from crowd-sourcing and expert surgeons. Segmentation of tasks to MPs results in the creation of the COMPASS dataset that nearly triples the amount of data for modeling and analysis and enables the generation of separate transcripts for the left and right tools. Conclusion: The proposed framework results in high quality labeling of surgical data based on context and fine-grained MPs. Modeling surgical tasks with MPs enables the aggregation of different datasets and the separate analysis of left and right hands for bimanual coordination assessment. Our formal framework and aggregate dataset can support the development of explainable and multi-granularity models for improved surgical process analysis, skill assessment, error detection, and autonomy.

翻译：目的：我们提出一种形式化框架，用于微创手术任务的建模与分割，采用统一的运动基元集合，以实现更客观的标注及多数据集的聚合。方法：我们将干式手术任务建模为有限状态机，表征运动基元作为基本手术操作的执行如何导致手术上下文的变化——这种变化刻画了手术环境中器械与物体间的物理交互。我们开发了基于视频数据的手术上下文标注方法，以及从上下文自动转换为运动基元标签的技术。随后利用该框架创建了上下文与运动基元聚合手术数据集，包含三个公开数据集中的六项干式手术任务，并提供运动学与视频数据及上下文与运动基元标签。结果：我们的上下文标注方法在众包标注与专家外科医生的一致性标签间达到近乎完美的吻合。将任务分割为运动基元后生成COMPASS数据集，其数据量近乎翻倍以支持建模与分析，并可生成左右器械的独立转录。结论：所提框架实现了基于上下文与细粒度运动基元的高质量手术数据标注。采用运动基元建模手术任务可聚合异构数据集，并支持左右手独立分析以评估双手协调性。我们的形式化框架与综合数据集能够推动可解释、多粒度模型的发展，从而优化手术流程分析、技能评估、错误检测及自主化水平。