Spartan: surgical peg-and-ring triplet and workflow anticipation benchmark

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Abstract
Purpose: Automation in robot-assisted surgery (RAS) requires not only accurate scene understanding but also real-time reasoning and action within dynamic surgical workflows. This work introduces SPARTAN: the S urgical P eg- A nd- R ing T riplet and Workflow AN ticipation Benchmark, alongside a unified baseline for real-time surgical workflow analysis, for the first time jointly addressing surgical phase recognition, phase anticipation, and action triplet recognition. This integrated design bridges high-level workflow understanding with fine-grained, robot-action-level perception. Methods: The SPARTAN benchmark is based on a modified Peg-and-Ring training task performed on the da Vinci Research Kit (dVRK), providing frame-level annotations of surgical phases and dual-arm action triplets that delineate initial, intermediate, and final workflow states. Results: We demonstrate that our baseline achieves performance comparable to state-of-the-art methods across all three SPARTAN tasks while operating in real time. The benchmark offers complexity comparable to related datasets in terms of phase structure, number of videos, and triplet diversity, yet remains reproducible and directly applicable to physical robotic systems. Conclusion: SPARTAN provides a practical foundation for developing and evaluating real-time perception and reasoning models in RAS.
Funding Information
  • Università degli Studi di Verona

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