Song Yiming, Zhang Zhengjie, Wang Ruilan, Zhong Ling, Cai Crystal, Chen Jinnan, Zhou Yujie, Wang Xinyuan, Li Zhao, Yang Liuyi, Li Zeyu, Yan Hao, Zhang Qingwei, Qian Dahong, Li Xiaobo
Division of Gastroenterology and Hepatology, Shanghai Institute of Digestive Disease, NHC Key Laboratory of Digestive Diseases, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Sci Data. 2025 Aug 7;12(1):1382. doi: 10.1038/s41597-025-05588-3.
Artificial intelligence (AI) holds immense potential to transform gastrointestinal endoscopy by reducing manual workload and enhancing procedural efficiency. However, the development of robust AI algorithms is hindered by limited access to high-quality medical datasets and the labor-intensive nature of data annotation. Here, we present CAS-Colon, a novel dataset comprising 78 high-resolution colonoscopy videos captured during the withdrawal phase. Each video is meticulously annotated with ten distinct anatomical regions and accompanied by comprehensive metadata. To our knowledge, CAS-Colon represents the largest and most detailed colonoscopy anatomical segmentation dataset available. This resource aims to accelerate the development of advanced AI algorithms and unlock the full potential of colonoscopy technology.
人工智能（AI）通过减少人工工作量和提高操作效率，在改变胃肠内镜检查方面具有巨大潜力。然而，高质量医学数据集的获取受限以及数据标注的劳动密集性阻碍了强大的AI算法的开发。在此，我们展示了CAS - 结肠数据集，这是一个新颖的数据集，包含在退镜阶段拍摄的78个高分辨率结肠镜检查视频。每个视频都精心标注了十个不同的解剖区域，并附有全面的元数据。据我们所知，CAS - 结肠数据集是现有的最大且最详细的结肠镜检查解剖分割数据集。该资源旨在加速先进AI算法的开发，并释放结肠镜检查技术的全部潜力。
