A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery

Narges Ahmidi, Lingling Tao, Shahin Sefati, Yixin Gao, Colin Lea, Benjamin Bejar Haro, Luca Zappella, Sanjeev Khudanpur, Rene Vidal, Gregory Hager

Research output: Contribution to journalArticle

Abstract

Objective: State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging. Methods: In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset that we have created to support comparative research benchmarking. JIGSAWS contains synchronized video and kinematic data from multiple performances of robotic surgical tasks by operators of varying skill. We address the latter by presenting a well-documented evaluation methodology and reporting results for six techniques for automated segmentation and classification of time-series data on JIGSAWS. These techniques comprise four temporal approaches for joint segmentation and classification: hidden Markov model, sparse hidden Markov model (HMM), Markov semi-Markov conditional random field, and skip-chain conditional random field; and two feature-based ones that aim to classify fixed segments: bag of spatiotemporal features and linear dynamical systems. Results: Most methods recognize gesture activities with approximately 80% overall accuracy under both leave-one-super-trial-out and leave-one-user-out cross-validation settings. Conclusion: Current methods show promising results on this shared dataset, but room for significant progress remains, particularly for consistent prediction of gesture activities across different surgeons. Significance: The results reported in this paper provide the first systematic and uniform evaluation of surgical activity recognition techniques on the benchmark database.

Original languageEnglish (US)
Article number7805258
Pages (from-to)2025-2041
Number of pages17
JournalIEEE Transactions on Biomedical Engineering
Volume64
Issue number9
DOIs
StatePublished - Sep 1 2017

Keywords

  • Activity recognition
  • benchmark robotic dataset
  • kinematics and video
  • surgical motion

ASJC Scopus subject areas

  • Biomedical Engineering

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    Ahmidi, N., Tao, L., Sefati, S., Gao, Y., Lea, C., Haro, B. B., Zappella, L., Khudanpur, S., Vidal, R., & Hager, G. (2017). A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery. IEEE Transactions on Biomedical Engineering, 64(9), 2025-2041. [7805258]. https://doi.org/10.1109/TBME.2016.2647680