Lesion-centered 3D mapping of colonoscopy procedures: validation of a hierarchical ensemble pipeline on public benchmark videos
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Abstract (arXiv)
Background and Objective: Colonoscopy recording practice preserves text reports and still photographs, while the spatial information already present in the recorded video - where the scope traveled, where a lesion was observed, and whether the same lesion was seen again - is discarded when the procedure ends. This study determines whether a lesion-centered spatial record can be assembled and validated without full-colon 3D reconstruction. Methods: A four-layer hierarchical pipeline was assembled - (1) a global topological map, (2) lesion-level spatio-temporal tracks, (3) on-demand local 3D reconstruction, and (4) persistent lesion identity across repeated observations - and ran end to end on four public videos (two C3VDv2 sequences with ground-truth depth and two full REAL-Colon procedures; 40,245 frames). All components are published, individually validated methods; the contribution is their lesion-centered assembly, linking rules, and evaluation. Results: Revisits, impossible under forward-only mapping by construction, were detected by entry-map Bayesian localization: 5,614 and 4,043 revisit events (56 and 68 distinct nodes) in the two full procedures. Lesion-identity merging at the adopted threshold 0.5 maintained ground-truth purity 1.0 while auto-merging 20 of 231 candidate pairs. The endoscopy-specific geometry engine outperformed a general-purpose foundation model on all metrics (overall absolute relative error (AbsRel) 0.2276 vs. 0.3523). Conclusions: The results are partial but establish a concrete near-term path: revisit detection, lesion identity, and local 3D each returned quantitative, reproducible output without waiting for complete geometric reconstruction; validating the record on clinical data is the next step.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 27 references that are in the map. First posted on arXiv 15 Sep 2026.
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21 of 27 references.
References · 21
- 2024SAM 2: Segment Anything in Images and Videoscited by 2,863
- 2025VGGT: Visual Geometry Grounded Transformercited by 1,539
- 2023LightGlue: Local Feature Matching at Light Speedcited by 590
- 2022Video Polyp Segmentation: A Deep Learning Perspectivecited by 82
- 2022Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registrationcited by 80
- 2023Foundation Model for Endoscopy Video Analysis via Large-scale Self-supervised Pre-traincited by 75
- 2022EndoMapper dataset of complete calibrated endoscopy procedurescited by 53
- 2019DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequencescited by 37
- 2024REAL-Colon: A dataset for developing real-world AI applications in colonoscopycited by 20
- 2022Semantic-SuPer: A Semantic-aware Surgical Perception Framework for Endoscopic Tissue Identification, Reconstruction, and Trackingcited by 19
- 2024SALI: Short-term Alignment and Long-term Interaction Network for Colonoscopy Video Polyp Segmentationcited by 19
- 2025Endo3R: Unified Online Reconstruction from Dynamic Monocular Endoscopic Videocited by 16
9 more
- 2025C3VDv2 -- Colonoscopy 3D video dataset with enhanced realismcited by 15
- 2023NR-SLAM: Non-Rigid Monocular SLAMcited by 11
- 2024CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopycited by 11
- 2024Topological SLAM in colonoscopies leveraging deep features and topological priorscited by 6
- 2023ColonMapper: topological mapping and localization for colonoscopycited by 5
- 20253D Densification for Multi-Map Monocular VSLAM in Endoscopycited by 3
- 2025PERSEUS: Perception with Semantic Endoscopic Understanding and SLAMcited by 3
- 2025EndoStreamDepth: Temporally Consistent Monocular Depth Estimation for Endoscopic Video Streamscited by 2
- 2026ColonSplat: Reconstruction of Peristaltic Motion in Colonoscopy with Dynamic Gaussian Splattingcited by 2
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