TITLE: COMPASS: A Formal Framework and Aggregate Dataset for Generalized Surgical Procedure Modeling 

ABSTRACT: 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.
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.
COMPASS: A Formal Framework and Aggregate Dataset for Generalized Surgical Procedure Modeling
http://arxiv.org/abs/2209.06424v5