COMPASS: a formal framework and aggregate dataset for generalized surgical procedure modeling
COMPASS: a formal framework and aggregate dataset for generalized surgical procedure modeling
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DOI：
10.1007/s11548-023-02922-1
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发表时间：
2022-09
影响因子：
3
通讯作者：
Kay Hutchinson;Ian Reyes;Zongyu Li;H. Alemzadeh
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文献类型：
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作者：
Kay Hutchinson;Ian Reyes;Zongyu Li;H. Alemzadeh
PurposeWe提出了一个正式的框架，用于建模和分割的微创手术任务，使用一组统一的运动原语（MP），使更多的客观标签和聚合的不同datasets.MethodsWe模型干实验室手术任务作为有限状态机，表示如何执行的MP作为基本的手术行动的结果在手术上下文的变化，其表征手术环境中工具和物体之间的物理相互作用。我们开发了基于视频数据标记手术上下文和将上下文自动翻译为MP标签的方法。然后，我们使用我们的框架来创建的COontext和运动原语聚合手术集（COMPASS），包括六个干实验室手术任务，从三个公开的数据集（JIGSAWS，DESK，和ROSMA），与运动学和视频数据和上下文和MP labels.ResultsOur上下文标签方法实现了近乎完美的协议之间的共识标签从众包和专家外科医生。分割的任务MP的结果在创建的COMPASS数据集，几乎三倍的建模和分析的数据量，并使单独的成绩单为左和右tools.ConclusionThe建议的框架结果在高质量的标签的手术数据的基础上上下文和细粒度的MP。使用MP对手术任务进行建模，可以聚合不同的数据集，并对左右手进行单独分析，以进行双手协调评估。我们的正式框架和聚合数据集可以支持可解释和多粒度模型的开发，以改进手术过程分析，技能评估，错误检测和自主性。
PurposeWe 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.MethodsWe 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.ResultsOur 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.ConclusionThe 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.