CAS-Colon: A Comprehensive Colonoscopy Anatomical Segmentation Dataset for Artificial Intelligence Development.

Journal: Scientific data
Published Date: Aug 7, 2025

Abstract

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.

Authors

  • Yiming Song
    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.
  • Zhengjie Zhang
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Ruilan Wang
    Department of Critical Care Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, No. 650 New Songjiang Road, Songjiang, Shanghai, 201600, China.
  • Ling Zhong
    Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education, Chongqing University, Chongqing 400044, China; College of Automation, Chongqing University, Chongqing 400044, China.
  • Crystal Cai
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • Jinnan Chen
    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.
  • Yujie Zhou
    School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China.
  • Xinyuan Wang
    Proteomics and Metabolomics Core Facilities, West China Hospital, Sichuan University, Chengdu 610041, China.
  • Zhao Li
    Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou, China. lzjoey@gmail.com.
  • Liuyi Yang
    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.
  • Zeyu Li
    Department of automation, Harbin Engineering University, China. Electronic address: zyLee1@126.com.
  • Hao Yan
    Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, Texas 75235.
  • Qingwei Zhang
    Division 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 Disease, Shanghai, China. Electronic address: zhangqingweif@hotmail.com.
  • Dahong Qian
  • Xiaobo Li
    Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, United States.

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