C3VDv2 - Colonoscopy 3D video dataset with enhanced realism.

Mayank V Golhar, Lucas Sebastian Galeano Fretes, Loren Ayers, Venkata S Akshintala, Taylor L Bobrow, Nicholas J Durr

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In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Mayank V GolharDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US.

Lucas Sebastian Galeano FretesDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US.ORCID http://orcid.org/0009-0000-7444-2889

Loren AyersDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US.

Venkata S AkshintalaDivision of Gastroenterology and Hepatology, Johns Hopkins Medicine, Baltimore, MD, US.

Taylor L BobrowDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US.

Nicholas J DurrDepartment of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, US. ndurr@jhu.edu.ORCID http://orcid.org/0000-0001-9808-7383

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Spatial computer vision techniques have the potential to improve the diagnostic performance of colonoscopy. However, the lack of 3D colonoscopy datasets for training and validation hinders their development. This paper introduces C3VDv2, the second version (v2) of the high-definition Colonoscopy 3D Video Dataset, featuring enhanced realism designed to facilitate the quantitative evaluation of 3D colon reconstruction algorithms. 192 video sequences totaling 169,371 frames were captured by imaging 60 unique, high-fidelity silicone colon phantom segments. Ground truth depth, surface normals, optical flow, occlusion, diffuse maps, six-degree-of-freedom pose, coverage map, and 3D models are provided for 169 colonoscopy videos. Eight simulated screening colonoscopy videos acquired by a gastroenterologist are provided with ground truth poses. Lastly, the dataset includes 15 videos with colon deformations for qualitative assessment. C3VDv2 emulates diverse and challenging scenarios for 3D reconstruction algorithms, including fecal debris, mucous pools, blood, debris obscuring the colonoscope lens, en-face views, and fast camera motion. The enhanced realism of C3VDv2 will allow for more robust and representative development and evaluation of 3D reconstruction algorithms.

Indexed as

ColonoscopyImaging, Three-DimensionalAlgorithmsHumansPhantoms, ImagingVideo Recording

Identifiers

PMID42236709

DOI10.1038/s41597-026-07471-1

PMCPMC13538448

What Socratic holds

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Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.