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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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
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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| Summary: | 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. |
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| ISSN: | 2052-4463 |