Expanded Comprehensive Robotic Cholecystectomy Dataset (CRCD)
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910747959754752 |
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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 Žefran, Miloš 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 Žefran, Miloš Chen, Liaohai Giulianotti, Pier Cristoforo |
| contents | In recent years, the application of machine learning to minimally invasive surgery (MIS) has attracted considerable interest. Datasets are critical to the use of such techniques. This paper presents a unique dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers using the da Vinci Research Kit (dVRK). Unlike existing datasets, it addresses a critical gap by providing comprehensive kinematic data, recordings of all pedal inputs, and offers a time-stamped record of the endoscope's movements. This expanded version also includes segmentation and keypoint annotations of images, enhancing its utility for computer vision applications.
Contributed by seven surgeons with varied backgrounds and experience levels that are provided as a part of this expanded version, the dataset is an important new resource for surgical robotics research. It enables the development of advanced methods for evaluating surgeon skills, tools for providing better context awareness, and automation of surgical tasks. Our work overcomes the limitations of incomplete recordings and imprecise kinematic data found in other datasets. To demonstrate the potential of the dataset for advancing automation in surgical robotics, we introduce two models that predict clutch usage and camera activation, a 3D scene reconstruction example, and the results from our keypoint and segmentation models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_12238 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Expanded Comprehensive Robotic Cholecystectomy Dataset (CRCD) Oh, Ki-Hwan Borgioli, Leonardo Mangano, Alberto Valle, Valentina Di Pangrazio, Marco Toti, Francesco Pozza, Gioia Ambrosini, Luciano Ducas, Alvaro Žefran, Miloš Chen, Liaohai Giulianotti, Pier Cristoforo Robotics In recent years, the application of machine learning to minimally invasive surgery (MIS) has attracted considerable interest. Datasets are critical to the use of such techniques. This paper presents a unique dataset recorded during ex vivo pseudo-cholecystectomy procedures on pig livers using the da Vinci Research Kit (dVRK). Unlike existing datasets, it addresses a critical gap by providing comprehensive kinematic data, recordings of all pedal inputs, and offers a time-stamped record of the endoscope's movements. This expanded version also includes segmentation and keypoint annotations of images, enhancing its utility for computer vision applications. Contributed by seven surgeons with varied backgrounds and experience levels that are provided as a part of this expanded version, the dataset is an important new resource for surgical robotics research. It enables the development of advanced methods for evaluating surgeon skills, tools for providing better context awareness, and automation of surgical tasks. Our work overcomes the limitations of incomplete recordings and imprecise kinematic data found in other datasets. To demonstrate the potential of the dataset for advancing automation in surgical robotics, we introduce two models that predict clutch usage and camera activation, a 3D scene reconstruction example, and the results from our keypoint and segmentation models. |
| title | Expanded Comprehensive Robotic Cholecystectomy Dataset (CRCD) |
| topic | Robotics |
| url | https://arxiv.org/abs/2412.12238 |