A surgical dataset from the da Vinci Research Kit for task automation and recognition (2102.03643v2)
Abstract: The use of datasets is getting more relevance in surgical robotics since they can be used to recognise and automate tasks. Also, this allows to use common datasets to compare different algorithms and methods. The objective of this work is to provide a complete dataset of three common training surgical tasks that surgeons perform to improve their skills. For this purpose, 12 subjects teleoperated the da Vinci Research Kit to perform these tasks. The obtained dataset includes all the kinematics and dynamics information provided by the da Vinci robot (both master and slave side) together with the associated video from the camera. All the information has been carefully timestamped and provided in a readable csv format. A MATLAB interface integrated with ROS for using and replicating the data is also provided.
- S. S. Vedula and G. D. Hager, “Surgical data science: The new knowledge domain,” Innovative Surgical Sciences, vol. 2, no. 3, pp. 109–121, 9 2020.
- C. J. Pérez-del Pulgar, J. Smisek, I. Rivas-Blanco, A. Schiele, and V. F. Muñoz, “Using Gaussian Mixture Models for Gesture Recognition During Haptically Guided Telemanipulation,” Electronics, vol. 8, no. 7, p. 772, 7 2019.
- N. Ahmidi, L. Tao, S. Sefati, Y. Gao, C. Lea, B. B. Haro, L. Zappella, S. Khudanpur, R. Vidal, and G. D. Hager, “A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery,” IEEE Transactions on Biomedical Engineering, vol. 64, no. 9, pp. 2025–2041, 9 2017.
- F. Setti, E. Oleari, A. Leporini, D. Trojaniello, A. Sanna, U. Capitanio, F. Montorsi, A. Salonia, and R. Muradore, “A Multirobots Teleoperated Platform for Artificial Intelligence Training Data Collection in Minimally Invasive Surgery,” in 2019 International Symposium on Medical Robotics, ISMR 2019. Institute of Electrical and Electronics Engineers Inc., 5 2019.
- I. Rivas-Blanco, C. J. Perez-Del-Pulgar, I. Garcia-Morales, V. F. Munoz, and I. Rivas-Blanco, “A Review on Deep Learning in Minimally Invasive Surgery,” IEEE Access, vol. 9, pp. 48 658–48 678, 2021.
- Y. Gao, S. S. Vedula, C. E. Reiley, N. Ahmidi, B. Varadarajan, H. C. Lin, L. Tao, L. Zappella, B. Béjar, D. D. Yuh, C. C. G. Chen, R. Vidal, S. Khudanpur, and G. D. Hager, “JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS): A Surgical Activity Dataset for Human Motion Modeling,” Modeling and Monitoring of Computer Assisted Interventions (M2CAI) – MICCAI Workshop, pp. 1–10, 2014.
- E. Colleoni, P. Edwards, and D. Stoyanov, “Synthetic and Real Inputs for Tool Segmentation in Robotic Surgery,” in International Conference on Medical Image Computing and Computer-Assisted Intervention - MICCAI 2020. Lima, Peru: Springer, Cham, 10 2020, pp. 700–710. [Online]. Available: https://link.springer.com/chapter/10.1007/978-3-030-59716-0_67
- P. Kazanzides, Z. Chen, A. Deguet, G. S. Fischer, R. H. Taylor, and S. P. Dimaio, “An Open-Source Research Kit for the da Vinci R Surgical System,” in IEEE International Conference on Robotics & Automation (ICRA), Hong Kong, China, 2014, pp. 6434–6439.
- Z. Chen, A. Deguet, R. H. Taylor, and P. Kazanzides, “Software architecture of the da vinci research kit,” in Proceedings - 2017 1st IEEE International Conference on Robotic Computing, IRC 2017. Institute of Electrical and Electronics Engineers Inc., 5 2017, pp. 180–187.
- G. A. Fontanelli, F. Ficuciello, L. Villani, and B. Siciliano, “Modelling and identification of the da Vinci Research Kit robotic arms,” in IEEE International Conference on Intelligent Robots and Systems, vol. 2017-Septe. Institute of Electrical and Electronics Engineers Inc., 12 2017, pp. 1464–1469.
- S. F. Hardon, T. Horeman, H. J. Bonjer, and W. J. Meijerink, “Force-based learning curve tracking in fundamental laparoscopic skills training,” Surgical Endoscopy, vol. 32, no. 8, pp. 3609–3621, 8 2018.
- C. A. Velasquez, N. V. Navkar, A. Alsaied, S. Balakrishnan, J. Abinahed, A. A. Al-Ansari, and W. Jong Yoon, “Preliminary design of an actuated imaging probe for generation of additional visual cues in a robotic surgery,” Surgical Endoscopy, vol. 30, no. 6, pp. 2641–2648, 6 2016.
- I. Rivas-Blanco, C. Pérez-del Pulgar, A. Mariani, and G. Tortora, “Training dataset from the Da Vinci Research Kit,” 5 2020. [Online]. Available: https://zenodo.org/record/3932964