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 D. Hager
2017 IEEE Transactions on Biomedical Engineering  
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: (1) lack of uniform shared datasets and benchmarks and (2) lack of consistent validation
more » ... processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset 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 HMM, Markov semi-Markov Conditional Random Field, and Skip-Chain CRF; 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.
doi:10.1109/tbme.2016.2647680 pmid:28060703 pmcid:PMC5559351 fatcat:xkhuzvvi7zckrf6at3dint7nsy