Comprehensive Robotic Cholecystectomy Dataset (CRCD): Integrating Kinematics, Pedal Signals, and Endoscopic Videos
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arXiv
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| Format: | Preprint |
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2023
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| author | Oh, Ki-Hwan Borgioli, Leonardo Mangano, Alberto Valle, Valentina Di Pangrazio, Marco Toti, Francesco Pozza, Gioia Ambrosini, Luciano Ducas, Alvaro Zefran, Milos Chen, Liaohai Giulianotti, Pier Cristoforo |
| author_facet | Oh, Ki-Hwan Borgioli, Leonardo Mangano, Alberto Valle, Valentina Di Pangrazio, Marco Toti, Francesco Pozza, Gioia Ambrosini, Luciano Ducas, Alvaro Zefran, Milos Chen, Liaohai Giulianotti, Pier Cristoforo |
| contents | In recent years, the potential applications of machine learning to Minimally Invasive Surgery (MIS) have spurred interest in data sets that can be used to develop data-driven tools. This paper introduces a novel dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers, utilizing the da Vinci Research Kit (dVRK). Unlike current datasets, ours bridges a critical gap by offering not only full kinematic data but also capturing all pedal inputs used during the procedure and providing a time-stamped record of the endoscope's movements. Contributed by seven surgeons, this data set introduces a new dimension to surgical robotics research, allowing the creation of advanced models for automating console functionalities. Our work addresses the existing limitation of incomplete recordings and imprecise kinematic data, common in other datasets. By introducing two models, dedicated to predicting clutch usage and camera activation, we highlight the dataset's potential for advancing automation in surgical robotics. The comparison of methodologies and time windows provides insights into the models' boundaries and limitations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_01183 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Comprehensive Robotic Cholecystectomy Dataset (CRCD): Integrating Kinematics, Pedal Signals, and Endoscopic Videos Oh, Ki-Hwan Borgioli, Leonardo Mangano, Alberto Valle, Valentina Di Pangrazio, Marco Toti, Francesco Pozza, Gioia Ambrosini, Luciano Ducas, Alvaro Zefran, Milos Chen, Liaohai Giulianotti, Pier Cristoforo Robotics In recent years, the potential applications of machine learning to Minimally Invasive Surgery (MIS) have spurred interest in data sets that can be used to develop data-driven tools. This paper introduces a novel dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers, utilizing the da Vinci Research Kit (dVRK). Unlike current datasets, ours bridges a critical gap by offering not only full kinematic data but also capturing all pedal inputs used during the procedure and providing a time-stamped record of the endoscope's movements. Contributed by seven surgeons, this data set introduces a new dimension to surgical robotics research, allowing the creation of advanced models for automating console functionalities. Our work addresses the existing limitation of incomplete recordings and imprecise kinematic data, common in other datasets. By introducing two models, dedicated to predicting clutch usage and camera activation, we highlight the dataset's potential for advancing automation in surgical robotics. The comparison of methodologies and time windows provides insights into the models' boundaries and limitations. |
| title | Comprehensive Robotic Cholecystectomy Dataset (CRCD): Integrating Kinematics, Pedal Signals, and Endoscopic Videos |
| topic | Robotics |
| url | https://arxiv.org/abs/2312.01183 |