ArticleMedical image analysis2023
Colonoscopy 3D video dataset with paired depth from 2D-3D registration.
Article in Medical image analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
The trial behind it
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Who cites it
15 citing papers in PubMed.
- A Dataset for Depth in Robotic Endoscopy with Dynamic Scenarios (DRENDS).Scientific data · 2026Article
- Self-supervised monocular depth estimation for colonoscopy using normal-guided cross-attention.International journal of computer assisted radiology and surgery · 2026Article
- C3VDv2 - Colonoscopy 3D video dataset with enhanced realism.Scientific data · 2026Article
- LumenGSLAM: online physically based rendering with Gaussian Splatting for robust endoscopic reconstruction and tracking.International journal of computer assisted radiology and surgery · 2026Article
- Multi-modal monocular endoscopic depth and pose estimation with edge-guided self-supervision.International journal of computer assisted radiology and surgery · 2026Article
- Multi-contrast laser endoscopy for in vivo gastrointestinal imaging.Npj imaging · 2026Article
- Digs: diffusion-guided Gaussian Splatting for dynamic occlusion surgical scene reconstruction.International journal of computer assisted radiology and surgery · 2026Article
- EndoStreamDepth: Temporally Consistent Monocular Depth Estimation for Endoscopic Video Streams.Proceedings of machine learning research · 2026Article
- NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting.Advances in neural information processing systems · 2025Article
- SHADeS: self-supervised monocular depth estimation through non-Lambertian image decomposition.International journal of computer assisted radiology and surgery · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- Leveraging Near-Field Lighting for Monocular Depth Estimation from Endoscopy Videos.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2025Article
- Advances in Real-Time 3D Reconstruction for Medical Endoscopy.Journal of imaging · 2024Review
- Density clustering-based automatic anatomical section recognition in colonoscopy video using deep learning.Scientific reports · 2024Article
- How Artificial Intelligence Is Shaping Medical Imaging Technology: A Survey of Innovations and Applications.Bioengineering (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Screening colonoscopy is an important clinical application for several 3D computer vision techniques, including depth estimation, surface reconstruction, and missing region detection. However, the development, evaluation, and comparison of these techniques in real colonoscopy videos remain largely qualitative due to the difficulty of acquiring ground truth data. In this work, we present a Colonoscopy 3D Video Dataset (C3VD) acquired with a high definition clinical colonoscope and high-fidelity colon models for benchmarking computer vision methods in colonoscopy. We introduce a novel multimodal 2D-3D registration technique to register optical video sequences with ground truth rendered views of a known 3D model. The different modalities are registered by transforming optical images to depth maps with a Generative Adversarial Network and aligning edge features with an evolutionary optimizer. This registration method achieves an average translation error of 0.321 millimeters and an average rotation error of 0.159 degrees in simulation experiments where error-free ground truth is available. The method also leverages video information, improving registration accuracy by 55.6% for translation and 60.4% for rotation compared to single frame registration. 22 short video sequences were registered to generate 10,015 total frames with paired ground truth depth, surface normals, optical flow, occlusion, six degree-of-freedom pose, coverage maps, and 3D models. The dataset also includes screening videos acquired by a gastroenterologist with paired ground truth pose and 3D surface models. The dataset and registration source code are available at https://durr.jhu.edu/C3VD.
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Registered trials
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.