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

Yiming Song¹, Zhengjie Zhang², Ruilan Wang³

¹Division 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.

Scientific Data

|August 7, 2025

Summary

This study introduces CAS-Colon, a large dataset of annotated colonoscopy videos to improve artificial intelligence (AI) for gastrointestinal endoscopy. This resource aims to boost AI development for enhanced procedural efficiency.

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Artificial intelligence (AI) has the potential to significantly improve gastrointestinal endoscopy by reducing manual workload and increasing efficiency.
The progress of AI in this field is currently limited by the scarcity of high-quality medical datasets and the intensive effort required for data annotation.

Purpose of the Study:

To introduce CAS-Colon, a novel dataset designed to facilitate the development of advanced AI algorithms for colonoscopy.
To provide a comprehensive resource for AI research in gastrointestinal endoscopy.

Main Methods:

The study presents CAS-Colon, a dataset containing 78 high-resolution colonoscopy videos.
Videos were captured during the withdrawal phase of colonoscopies.
Each video is meticulously annotated with ten distinct anatomical regions and includes comprehensive metadata.

Main Results:

CAS-Colon is the largest and most detailed colonoscopy anatomical segmentation dataset currently available.
The dataset provides a valuable resource for training and validating AI algorithms.

Conclusions:

The CAS-Colon dataset is expected to accelerate the development of AI-powered tools for colonoscopy.
This resource has the potential to unlock the full capabilities of AI in gastrointestinal endoscopy, leading to improved patient care.