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Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists

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C3VDv2 - Colonoscopy 3D video dataset with enhanced realism.

Mayank V Golhar¹, Lucas Sebastian Galeano Fretes¹, Loren Ayers¹

¹Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US.

Scientific Data

|June 3, 2026

Summary

The Colonoscopy 3D Video Dataset (C3VDv2) enhances realism for training 3D reconstruction algorithms. This new dataset aids in developing better spatial computer vision for improved colonoscopy diagnostics.

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Area of Science:

Medical Imaging
Computer Vision
Robotics

Background:

Spatial computer vision holds promise for enhancing colonoscopy diagnostic accuracy.
Current development of these techniques is limited by the scarcity of 3D colonoscopy datasets for training and validation.

Purpose of the Study:

Introduce C3VDv2, an enhanced high-definition 3D colonoscopy video dataset.
Facilitate quantitative evaluation of 3D colon reconstruction algorithms with improved realism.

Main Methods:

Captured 192 video sequences (169,371 frames) from 60 silicone colon phantom segments.
Provided ground truth data including depth, surface normals, optical flow, pose, and 3D models for 169 videos.
Included simulated screening videos and videos with colon deformations for comprehensive assessment.

Main Results:

C3VDv2 offers enhanced realism, simulating challenging scenarios like debris, mucous, blood, and rapid motion.
The dataset provides extensive ground truth for robust algorithm development and evaluation.
Includes data for both quantitative and qualitative assessments of 3D reconstruction.

Conclusions:

C3VDv2 significantly advances the development and evaluation of 3D reconstruction algorithms for colonoscopy.
The dataset's realism enables more robust and representative training and validation processes.
Facilitates the advancement of spatial computer vision applications in endoscopic procedures.