A Comprehensive Video Dataset for Surgical Laparoscopic Action Analysis
Abstract Laparoscopic surgery has been widely used in various surgical fields due to its minimally invasive and rapid recovery benefits. However, it demands a high level of technical expertise from surgeons. While advancements in computer vision and deep learning have significantly contributed to su...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05093-7 |
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| author | Zi Ye Ru Zhou Zili Deng Dan Wang Ying Zhu Xiaoli Jin Lijun Zhang Tianxiang Chen Hanwei Zhang Mingliang Wang |
| author_facet | Zi Ye Ru Zhou Zili Deng Dan Wang Ying Zhu Xiaoli Jin Lijun Zhang Tianxiang Chen Hanwei Zhang Mingliang Wang |
| author_sort | Zi Ye |
| collection | DOAJ |
| description | Abstract Laparoscopic surgery has been widely used in various surgical fields due to its minimally invasive and rapid recovery benefits. However, it demands a high level of technical expertise from surgeons. While advancements in computer vision and deep learning have significantly contributed to surgical action recognition, the effectiveness of these technologies is hindered by the limitations of existing publicly available datasets, such as their small scale, high homogeneity, and inconsistent labeling quality. To address the above issues, we developed the SLAM dataset (Surgical LAparoscopic Motions), which encompasses various surgical types such as laparoscopic cholecystectomy and appendectomy. The dataset includes annotations for seven key actions: Abdominal Entry, Use Clip, Hook Cut, Suturing, Panoramic View, Local Panoramic View, and Suction. In total, it includes 4,097 video clips, each labeled with corresponding action categories. In addition, we comprehensively validated the dataset using the ViViT model, and the experimental results showed that the dataset exhibited superior training and testing capabilities in laparoscopic surgical action recognition, with the highest classification accuracy of 85.90%. As a publicly available benchmark resource, the SLAM dataset aims to promote the development of laparoscopic surgical action recognition and artificial intelligence-driven surgery, supporting intelligent surgical robots and surgical automation. |
| format | Article |
| id | doaj-art-8eb5f0e165d94e0c9cb6bc1bfb1519e3 |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-8eb5f0e165d94e0c9cb6bc1bfb1519e32025-08-20T03:48:19ZengNature PortfolioScientific Data2052-44632025-05-0112111010.1038/s41597-025-05093-7A Comprehensive Video Dataset for Surgical Laparoscopic Action AnalysisZi Ye0Ru Zhou1Zili Deng2Dan Wang3Ying Zhu4Xiaoli Jin5Lijun Zhang6Tianxiang Chen7Hanwei Zhang8Mingliang Wang9Department of General Surgery, RuiJin Hospital LuWan Branch, Shanghai Jiaotong University School of MedicineDepartment of General Surgery, RuiJin Hospital LuWan Branch, Shanghai Jiaotong University School of MedicineHangzhou Institute for Advanced Study, University of Chinese Academy of SciencesHangzhou Institute for Advanced Study, University of Chinese Academy of SciencesHangzhou Institute for Advanced Study, University of Chinese Academy of SciencesDepartment of General Surgery, RuiJin Hospital LuWan Branch, Shanghai Jiaotong University School of MedicineInstitute of Software, Chinese Academy of SciencesSchool of Cyber Space and Technology, University of Science and Technology of ChinaInstitute of Intelligent Software, GuangzhouDepartment of General Surgery, RuiJin Hospital LuWan Branch, Shanghai Jiaotong University School of MedicineAbstract Laparoscopic surgery has been widely used in various surgical fields due to its minimally invasive and rapid recovery benefits. However, it demands a high level of technical expertise from surgeons. While advancements in computer vision and deep learning have significantly contributed to surgical action recognition, the effectiveness of these technologies is hindered by the limitations of existing publicly available datasets, such as their small scale, high homogeneity, and inconsistent labeling quality. To address the above issues, we developed the SLAM dataset (Surgical LAparoscopic Motions), which encompasses various surgical types such as laparoscopic cholecystectomy and appendectomy. The dataset includes annotations for seven key actions: Abdominal Entry, Use Clip, Hook Cut, Suturing, Panoramic View, Local Panoramic View, and Suction. In total, it includes 4,097 video clips, each labeled with corresponding action categories. In addition, we comprehensively validated the dataset using the ViViT model, and the experimental results showed that the dataset exhibited superior training and testing capabilities in laparoscopic surgical action recognition, with the highest classification accuracy of 85.90%. As a publicly available benchmark resource, the SLAM dataset aims to promote the development of laparoscopic surgical action recognition and artificial intelligence-driven surgery, supporting intelligent surgical robots and surgical automation.https://doi.org/10.1038/s41597-025-05093-7 |
| spellingShingle | Zi Ye Ru Zhou Zili Deng Dan Wang Ying Zhu Xiaoli Jin Lijun Zhang Tianxiang Chen Hanwei Zhang Mingliang Wang A Comprehensive Video Dataset for Surgical Laparoscopic Action Analysis Scientific Data |
| title | A Comprehensive Video Dataset for Surgical Laparoscopic Action Analysis |
| title_full | A Comprehensive Video Dataset for Surgical Laparoscopic Action Analysis |
| title_fullStr | A Comprehensive Video Dataset for Surgical Laparoscopic Action Analysis |
| title_full_unstemmed | A Comprehensive Video Dataset for Surgical Laparoscopic Action Analysis |
| title_short | A Comprehensive Video Dataset for Surgical Laparoscopic Action Analysis |
| title_sort | comprehensive video dataset for surgical laparoscopic action analysis |
| url | https://doi.org/10.1038/s41597-025-05093-7 |
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