Polyp-Size: A Precise Endoscopic Dataset for AI-Driven Polyp Sizing.

Journal: Scientific data
Published Date: May 31, 2025

Abstract

Colorectal cancer often arises from precancerous polyps, where accurate size assessment is vital for clinical decisions but challenged by subjective methods. While artificial intelligence (AI) has shown promise in improving the accuracy of polyp size estimation, its development depends on large, meticulously annotated datasets. We present Polyp-Size, a dataset of 42 high-resolution white-light colonoscopy videos with polyp sizes precisely measured post-resection using vernier calipers to submillimeter precision. Unlike existing datasets primarily focused on polyp detection or segmentation, Polyp-Size offers validated size annotations, diverse polyp features (Paris classification, anatomical location and histological type), and standardized video formats, enabling robust AI models for size estimation. By making this resource publicly available, we aim to foster research collaboration and innovation in automated polyp measurement to ultimately improve clinical practice.

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.
  • Sijia Du
    School of Biomedical Engineering, Shanghai Jiao Tong University, China; Department of Nuclear Medicine, Ruijin Hospital,Shanghai Jiao Tong University School of Medicine, 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.
  • Fei Liu
    Department of Interventional Radiology, Qinghai Red Cross Hospital, Xining, Qinghai, China.
  • Xiaolu Lin
    Department of Digestive Endoscopy Center, Fujian Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fuzhou, Fujian, 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.
  • Zeyu Li
    Department of automation, Harbin Engineering University, China. Electronic address: zyLee1@126.com.
  • 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.
  • Zhengjie Zhang
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
  • 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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