Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions
Abstract Artificial intelligence advancements have significantly enhanced computer-aided intervention, learning among surgeons, and analysis of surgical videos post-operation, substantially elevating surgical expertise and patient outcomes. Recognition systems for endoscopic surgical phases using de...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-025-05718-x |
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| _version_ | 1849388331443421184 |
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| author | Jinnan Chen Xiangning Zhang Jinneng Wang Tang Cao Chunjiang Gu Zhao Li Yiming Song Liuyi Yang Zhengjie Zhang Qingwei Zhang Dahong Qian Xiaobo Li |
| author_facet | Jinnan Chen Xiangning Zhang Jinneng Wang Tang Cao Chunjiang Gu Zhao Li Yiming Song Liuyi Yang Zhengjie Zhang Qingwei Zhang Dahong Qian Xiaobo Li |
| author_sort | Jinnan Chen |
| collection | DOAJ |
| description | Abstract Artificial intelligence advancements have significantly enhanced computer-aided intervention, learning among surgeons, and analysis of surgical videos post-operation, substantially elevating surgical expertise and patient outcomes. Recognition systems for endoscopic surgical phases using deep learning algorithms heavily rely on comprehensive annotated datasets. Our research presents the Renji dataset featuring videos of endoscopic submucosal dissection (ESD) for colorectal neoplastic lesions (CNLs), which includes 30 procedural recordings with 130,298 phase-specific annotations collaboratively labeled by a team of three specialists in endoscopy. To our knowledge, this represents the first openly accessible collection of ESD videos specifically targeting CNLs treatment, and we anticipate this work will help establish standards for constructing similar ESD databases. Both the video collection and corresponding annotations have been made publicly accessible through the Figshare platform. |
| format | Article |
| id | doaj-art-252fc7c7fcdf4a66b4ee3b561fa1d8bd |
| institution | Kabale University |
| issn | 2052-4463 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj-art-252fc7c7fcdf4a66b4ee3b561fa1d8bd2025-08-20T03:42:19ZengNature PortfolioScientific Data2052-44632025-08-011211710.1038/s41597-025-05718-xRenji endoscopic submucosal dissection video data set for colorectal neoplastic lesionsJinnan Chen0Xiangning Zhang1Jinneng Wang2Tang Cao3Chunjiang Gu4Zhao Li5Yiming Song6Liuyi Yang7Zhengjie Zhang8Qingwei Zhang9Dahong Qian10Xiaobo Li11Division of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive DiseaseSchool of Biomedical Engineering, Shanghai Jiao Tong UniversityDepartment of Gastroenterology, Beibei Hospital of Chongqing Medical University (The Ninth People’s Hospital of Chongqing)Department of Gastroenterology, The First Branch, The First Affiliated Hospital of Chongqing Medical UniversityDepartment of Gastroenterology, Liangping District Peoples Hospital of ChongqingDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive DiseaseDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive DiseaseDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive DiseaseSchool of Biomedical Engineering, Shanghai Jiao Tong UniversityDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive DiseaseSchool of Biomedical Engineering, Shanghai Jiao Tong UniversityDivision of Gastroenterology and Hepatology, Key Laboratory of Gastroenterology and Hepatology, Ministry of Health, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai Institute of Digestive DiseaseAbstract Artificial intelligence advancements have significantly enhanced computer-aided intervention, learning among surgeons, and analysis of surgical videos post-operation, substantially elevating surgical expertise and patient outcomes. Recognition systems for endoscopic surgical phases using deep learning algorithms heavily rely on comprehensive annotated datasets. Our research presents the Renji dataset featuring videos of endoscopic submucosal dissection (ESD) for colorectal neoplastic lesions (CNLs), which includes 30 procedural recordings with 130,298 phase-specific annotations collaboratively labeled by a team of three specialists in endoscopy. To our knowledge, this represents the first openly accessible collection of ESD videos specifically targeting CNLs treatment, and we anticipate this work will help establish standards for constructing similar ESD databases. Both the video collection and corresponding annotations have been made publicly accessible through the Figshare platform.https://doi.org/10.1038/s41597-025-05718-x |
| spellingShingle | Jinnan Chen Xiangning Zhang Jinneng Wang Tang Cao Chunjiang Gu Zhao Li Yiming Song Liuyi Yang Zhengjie Zhang Qingwei Zhang Dahong Qian Xiaobo Li Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions Scientific Data |
| title | Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions |
| title_full | Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions |
| title_fullStr | Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions |
| title_full_unstemmed | Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions |
| title_short | Renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions |
| title_sort | renji endoscopic submucosal dissection video data set for colorectal neoplastic lesions |
| url | https://doi.org/10.1038/s41597-025-05718-x |
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