{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T20:31:13Z","timestamp":1790886673274,"version":"4.1.0"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T00:00:00Z","timestamp":1780444800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":92,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100024146","name":"Olympus Corporation of the Americas","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100024146","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Data"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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\n                    <jats:italic>C3VDv2<\/jats:italic>\n                    , the second version (\n                    <jats:italic>v2<\/jats:italic>\n                    ) of the high-definition\n                    <jats:italic>C<\/jats:italic>\n                    olonoscopy\n                    <jats:italic>3D V<\/jats:italic>\n                    ideo\n                    <jats:italic>D<\/jats:italic>\n                    ataset, 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.\n                  <\/jats:p>","DOI":"10.1038\/s41597-026-07471-1","type":"journal-article","created":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T10:25:15Z","timestamp":1780482315000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["C3VDv2 - Colonoscopy 3D video dataset with enhanced realism"],"prefix":"10.1038","volume":"13","author":[{"given":"Mayank V.","family":"Golhar","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7444-2889","authenticated-orcid":false,"given":"Lucas Sebastian","family":"Galeano Fretes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Loren","family":"Ayers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Venkata S.","family":"Akshintala","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taylor L.","family":"Bobrow","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9808-7383","authenticated-orcid":false,"given":"Nicholas J.","family":"Durr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,3]]},"reference":[{"key":"7471_CR1","doi-asserted-by":"publisher","first-page":"10","DOI":"10.3322\/caac.21871","volume":"75","author":"RL Siegel","year":"2025","unstructured":"Siegel, R. L., Kratzer, T. B., Giaquinto, A. N., Sung, H. & Jemal, A. Cancer statistics, 2025. CA Cancer J. Clin. 75, 10, https:\/\/doi.org\/10.3322\/caac.21871 (2025).","journal-title":"CA Cancer J. Clin."},{"key":"7471_CR2","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/j.gie.2014.07.058","volume":"81","author":"DK Rex","year":"2015","unstructured":"Rex, D. K. et al. Quality indicators for colonoscopy. Gastrointest. Endosc. 81, 31\u201353, https:\/\/doi.org\/10.1016\/j.gie.2014.07.058 (2015).","journal-title":"Gastrointest. Endosc."},{"key":"7471_CR3","doi-asserted-by":"publisher","first-page":"687","DOI":"10.1056\/NEJMoa1100370","volume":"366","author":"AG Zauber","year":"2012","unstructured":"Zauber, A. G. et al. Colonoscopic polypectomy and long-term prevention of colorectal-cancer deaths. N. Engl. J. Med. 366, 687\u2013696, https:\/\/doi.org\/10.1056\/NEJMoa1100370 (2012).","journal-title":"N. Engl. J. Med."},{"key":"7471_CR4","doi-asserted-by":"publisher","first-page":"1661","DOI":"10.1053\/j.gastro.2019.01.260","volume":"156","author":"S Zhao","year":"2019","unstructured":"Zhao, S. et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis. Gastroenterology 156, 1661\u20131674, https:\/\/doi.org\/10.1053\/j.gastro.2019.01.260 (2019).","journal-title":"Gastroenterology"},{"key":"7471_CR5","doi-asserted-by":"publisher","first-page":"1284","DOI":"10.1055\/a-1346-7455","volume":"53","author":"SK McGill","year":"2021","unstructured":"McGill, S. K. et al. Artificial intelligence identifies and quantifies colonoscopy blind spots. Endoscopy 53, 1284\u20131286, https:\/\/doi.org\/10.1055\/a-1346-7455 (2021).","journal-title":"Endoscopy"},{"key":"7471_CR6","doi-asserted-by":"publisher","first-page":"102100","DOI":"10.1016\/j.media.2021.102100","volume":"72","author":"R Ma","year":"2021","unstructured":"Ma, R. et al. RNNSLAM: Reconstructing the 3D colon to visualize missing regions during a colonoscopy. Med. Image Anal. 72, 102100, https:\/\/doi.org\/10.1016\/j.media.2021.102100 (2021).","journal-title":"Med. Image Anal."},{"key":"7471_CR7","unstructured":"Chen, R. J. et al. Slam endoscopy enhanced by adversarial depth prediction. KDD Workshop on Applied Data Science for Healthcare https:\/\/arxiv.org\/abs\/1907.00283 (2019)."},{"key":"7471_CR8","doi-asserted-by":"publisher","unstructured":"Ma, R. et al. Real-time 3d reconstruction of colonoscopic surfaces for determining missing regions. In Proc. MICCAI 2019, 573\u2013582, https:\/\/doi.org\/10.1007\/978-3-030-32254-0_64 (Springer, 2019).","DOI":"10.1007\/978-3-030-32254-0_64"},{"key":"7471_CR9","doi-asserted-by":"publisher","first-page":"101900","DOI":"10.1016\/j.media.2020.101900","volume":"68","author":"S Ali","year":"2021","unstructured":"Ali, S. et al. A deep learning framework for quality assessment and restoration in video endoscopy. Med. Image Anal. 68, 101900, https:\/\/doi.org\/10.1016\/j.media.2020.101900 (2021).","journal-title":"Med. Image Anal."},{"key":"7471_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-59413-5","volume":"10","author":"S Ali","year":"2020","unstructured":"Ali, S. et al. An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy. Sci. Rep. 10, 2748, https:\/\/doi.org\/10.1038\/s41598-020-59413-5 (2020).","journal-title":"Sci. Rep."},{"key":"7471_CR11","doi-asserted-by":"publisher","first-page":"1167","DOI":"10.1007\/s11548-019-01962-w","volume":"14","author":"A Rau","year":"2019","unstructured":"Rau, A. et al. Implicit domain adaptation with conditional generative adversarial networks for depth prediction in endoscopy. Int. J. Comput. Assist. Radiol. Surg. 14, 1167\u20131176, https:\/\/doi.org\/10.1007\/s11548-019-01962-w (2019).","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"7471_CR12","doi-asserted-by":"publisher","first-page":"1018","DOI":"10.1109\/TMRB.2023.3320267","volume":"5","author":"A Rau","year":"2023","unstructured":"Rau, A., Bhattarai, B., Agapito, L. & Stoyanov, D. Bimodal camera pose prediction for endoscopy. IEEE Trans. Med. Robot. Bionics 5, 1018\u20131026, https:\/\/doi.org\/10.1109\/TMRB.2023.3320267 (2023).","journal-title":"IEEE Trans. Med. Robot. Bionics"},{"key":"7471_CR13","doi-asserted-by":"publisher","first-page":"103195","DOI":"10.1016\/j.media.2024.103195","volume":"96","author":"A Rau","year":"2024","unstructured":"Rau, A. et al. Simcol3d\u20143d reconstruction during colonoscopy challenge. Med. Image Anal. 96, 103195, https:\/\/doi.org\/10.1016\/j.media.2024.103195 (2024).","journal-title":"Med. Image Anal."},{"key":"7471_CR14","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.media.2018.06.005","volume":"48","author":"F Mahmood","year":"2018","unstructured":"Mahmood, F. & Durr, N. J. Deep learning and conditional random fields-based depth estimation and topographical reconstruction from conventional endoscopy. Med. Image Anal. 48, 230\u2013243, https:\/\/doi.org\/10.1016\/j.media.2018.06.005 (2018).","journal-title":"Med. Image Anal."},{"key":"7471_CR15","doi-asserted-by":"publisher","first-page":"102058","DOI":"10.1016\/j.media.2021.102058","volume":"71","author":"KB Ozyoruk","year":"2021","unstructured":"Ozyoruk, K. B. et al. EndoSLAM dataset and an unsupervised monocular visual odometry and depth estimation approach for endoscopic videos. Med. Image Anal. 71, 102058, https:\/\/doi.org\/10.1016\/j.media.2021.102058 (2021).","journal-title":"Med. Image Anal."},{"key":"7471_CR16","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02564-7","volume":"10","author":"P Azagra","year":"2023","unstructured":"Azagra, P. et al. Endomapper dataset of complete calibrated endoscopy procedures. Sci. Data 10, 671, https:\/\/doi.org\/10.1038\/s41597-023-02564-7 (2023).","journal-title":"Sci. Data"},{"key":"7471_CR17","doi-asserted-by":"publisher","unstructured":"Mahmood, F. & Durr, N. J. Deep learning-based depth estimation from a synthetic endoscopy image training set. In Proc. SPIE 10574, 521\u2013526, https:\/\/doi.org\/10.1117\/12.2293785 (2018).","DOI":"10.1117\/12.2293785"},{"key":"7471_CR18","doi-asserted-by":"publisher","first-page":"185012","DOI":"10.1088\/1361-6560\/aada93","volume":"63","author":"F Mahmood","year":"2018","unstructured":"Mahmood, F., Chen, R., Sudarsky, S., Yu, D. & Durr, N. J. Deep learning with cinematic rendering: fine-tuning deep neural networks using photorealistic medical images. Phys. Med. Biol. 63, 185012, https:\/\/doi.org\/10.1088\/1361-6560\/aada93 (2018).","journal-title":"Phys. Med. Biol."},{"key":"7471_CR19","doi-asserted-by":"publisher","first-page":"109672","DOI":"10.1016\/j.compbiomed.2025.109672","volume":"186","author":"D He","year":"2025","unstructured":"He, D. et al. Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation. Comput. Biol. Med. 186, 109672, https:\/\/doi.org\/10.1016\/j.compbiomed.2025.109672 (2025).","journal-title":"Comput. Biol. Med."},{"key":"7471_CR20","doi-asserted-by":"publisher","first-page":"102956","DOI":"10.1016\/j.media.2023.102956","volume":"90","author":"TL Bobrow","year":"2023","unstructured":"Bobrow, T. L. et al. Colonoscopy 3d video dataset with paired depth from 2d-3d registration. Med. Image Anal. 90, 102956, https:\/\/doi.org\/10.1016\/j.media.2023.102956 (2023).","journal-title":"Med. Image Anal."},{"key":"7471_CR21","doi-asserted-by":"publisher","unstructured":"Golhar, M. V. et al. Data associated with the publication: C3vdv2: 3d colonoscopy video dataset with enhanced realism. Johns Hopkins Research Data Repository., https:\/\/doi.org\/10.7281\/T1\/JC64MK (2025).","DOI":"10.7281\/T1\/JC64MK"},{"key":"7471_CR22","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1007\/s11548-024-03171-6","volume":"19","author":"T Teufel","year":"2024","unstructured":"Teufel, T. et al. Oneslam to map them all: a generalized approach to slam for monocular endoscopic imaging based on tracking any point. Int. J. Comput. Assist. Radiol. Surg. 19, 1259\u20131266, https:\/\/doi.org\/10.1007\/s11548-024-03171-6 (2024).","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"7471_CR23","unstructured":"Elvira, R., Tardos, J. D. & Montiel, J. M. Cudasift-slam: multiple-map visual slam for full procedure mapping in real human endoscopy. Preprint at https:\/\/arxiv.org\/abs\/2405.16932 (2024)."},{"key":"7471_CR24","doi-asserted-by":"publisher","unstructured":"Wang, S. et al. A surface-normal based neural framework for colonoscopy reconstruction. In Proc. IPMI 2023, 797-809, https:\/\/doi.org\/10.1007\/978-3-031-34048-2_61 (Springer, 2023).","DOI":"10.1007\/978-3-031-34048-2_61"},{"key":"7471_CR25","doi-asserted-by":"publisher","unstructured":"Batlle, V. M., Montiel, J. M., Fua, P. & Tardos, J. D. Lightneus: neural surface reconstruction in endoscopy using illumination decline. In Proc. MICCAI 2023, 502\u2013512, https:\/\/doi.org\/10.1007\/978-3-031-43999-5_48 (Springer, 2023).","DOI":"10.1007\/978-3-031-43999-5_48"},{"key":"7471_CR26","doi-asserted-by":"publisher","unstructured":"Shan, J., Li, Y., Xie, T. & Wang, H. Enerf-slam: a dense endoscopic slam with neural implicit representation. IEEE Trans. Med. Robot. Bionics, https:\/\/doi.org\/10.1109\/TMRB.2024.3407369 (2024).","DOI":"10.1109\/TMRB.2024.3407369"},{"key":"7471_CR27","unstructured":"Huang, B. et al. High-fidelity endoscopic image synthesis by utilizing depth-guided neural surfaces. Preprint at https:\/\/arxiv.org\/abs\/2404.13437 (2024)."},{"key":"7471_CR28","doi-asserted-by":"publisher","unstructured":"Bonilla, S. et al. Gaussian pancakes: geometrically-regularized 3d gaussian splatting for realistic endoscopic reconstruction. In Proc. MICCAI 2024, 274\u2013283, https:\/\/doi.org\/10.1007\/978-3-031-72089-5_26 (Springer, 2024).","DOI":"10.1007\/978-3-031-72089-5_26"},{"key":"7471_CR29","doi-asserted-by":"publisher","unstructured":"Wang, K. et al. Endogslam: real-time dense reconstruction and tracking in endoscopic surgeries using gaussian splatting. In Proc. MICCAI 2024, 219\u2013229, https:\/\/doi.org\/10.1007\/978-3-031-72089-5_21 (Springer, 2024).","DOI":"10.1007\/978-3-031-72089-5_21"},{"key":"7471_CR30","doi-asserted-by":"publisher","first-page":"103379","DOI":"10.1016\/j.media.2024.103379","volume":"99","author":"PEC Solano","year":"2025","unstructured":"Solano, P. E. C., Bulpitt, A., Subramanian, V. & Ali, S. Multi-task learning with cross-task consistency for improved depth estimation in colonoscopy. Med. Image Anal. 99, 103379, https:\/\/doi.org\/10.1016\/j.media.2024.103379 (2025).","journal-title":"Med. Image Anal."},{"key":"7471_CR31","doi-asserted-by":"publisher","unstructured":"Paruchuri, A. et al. Leveraging near-field lighting for monocular depth estimation from endoscopy videos. In Proc. ECCV 2024, 473\u2013491, https:\/\/doi.org\/10.1007\/978-3-031-73411-3_27 (Springer, 2024).","DOI":"10.1007\/978-3-031-73411-3_27"},{"key":"7471_CR32","doi-asserted-by":"publisher","first-page":"7225","DOI":"10.1109\/LRA.2021.3095528","volume":"6","author":"D Recasens","year":"2021","unstructured":"Recasens, D., Lamarca, J., Facil, J. M., Montiel, J. & Civera, J. Endo-depth-and-motion: reconstruction and tracking in endoscopic videos using depth networks and photometric constraints. IEEE Robot. Autom. Lett. 6, 7225\u20137232, https:\/\/doi.org\/10.1109\/LRA.2021.3095528 (2021).","journal-title":"IEEE Robot. Autom. Lett."},{"key":"7471_CR33","doi-asserted-by":"publisher","unstructured":"Batlle, V. M., Montiel, J. M. & Tardos, J. D. Photometric single-view dense 3d reconstruction in endoscopy. In Proc. IROS 2022, 4904\u20134910, https:\/\/doi.org\/10.1109\/IROS47612.2022.9981742 (IEEE, 2022).","DOI":"10.1109\/IROS47612.2022.9981742"},{"key":"7471_CR34","doi-asserted-by":"publisher","unstructured":"Huang, B. et al. Self-supervised depth estimation in laparoscopic image using 3d geometric consistency. In Proc. MICCAI 2022, 13\u201322, https:\/\/doi.org\/10.1007\/978-3-031-16449-1_2 (Springer, 2022).","DOI":"10.1007\/978-3-031-16449-1_2"},{"key":"7471_CR35","doi-asserted-by":"publisher","first-page":"6078","DOI":"10.1109\/JBHI.2024.3423791","volume":"28","author":"Q He","year":"2024","unstructured":"He, Q., Feng, G., Bano, S., Stoyanov, D. & Zuo, S. Monolot: self-supervised monocular depth estimation in low-texture scenes for automatic robotic endoscopy. IEEE J. Biomed. Health Inform. 28, 6078\u20136091, https:\/\/doi.org\/10.1109\/JBHI.2024.3423791 (2024).","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"7471_CR36","doi-asserted-by":"publisher","unstructured":"Rodriguez, J. J. G., Montiel, J. M. & Tardos, J. D. Tracking monocular camera pose and deformation for slam inside the human body. In Proc. IROS 2022, 5278\u20135285, https:\/\/doi.org\/10.1109\/IROS47612.2022.9981203 (IEEE, 2022).","DOI":"10.1109\/IROS47612.2022.9981203"},{"key":"7471_CR37","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1093\/jcag\/gwad017","volume":"6","author":"M Taghiakbari","year":"2023","unstructured":"Taghiakbari, M. et al. Automated detection of anatomical landmarks during colonoscopy using a deep learning model. J. Can. Assoc. Gastroenterol. 6, 145\u2013151, https:\/\/doi.org\/10.1093\/jcag\/gwad017 (2023).","journal-title":"J. Can. Assoc. Gastroenterol."},{"key":"7471_CR38","unstructured":"Mahmood, F., Yang, Z., Ashley, T. & Durr, N. J. Multimodal densenet. Preprint athttps:\/\/arxiv.org\/abs\/1811.07407 (2018)."},{"key":"7471_CR39","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1136\/gutjnl-2021-324510","volume":"71","author":"M Abdelrahim","year":"2022","unstructured":"Abdelrahim, M. et al. Automated sizing of colorectal polyps using computer vision. Gut 71, 7\u20139, https:\/\/doi.org\/10.1136\/gutjnl-2021-324510 (2022).","journal-title":"Gut"},{"key":"7471_CR40","doi-asserted-by":"publisher","unstructured":"Iranzo, R., Batlle, V. M., Tardos, J. D. & Montiel, J. M. Endometric: near-light monocular metric scale estimation in endoscopy. In Proc. MICCAI 2025, https:\/\/doi.org\/10.1007\/978-3-032-05127-1_18 (2025).","DOI":"10.1007\/978-3-032-05127-1_18"},{"key":"7471_CR41","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1093\/gastro\/gov027","volume":"3","author":"N Mahmud","year":"2015","unstructured":"Mahmud, N., Cohen, J., Tsourides, K. & Berzin, T. M. Computer vision and augmented reality in gastrointestinal endoscopy. Gastroenterol. Rep. 3, 179\u2013184, https:\/\/doi.org\/10.1093\/gastro\/gov027 (2015).","journal-title":"Gastroenterol. Rep."},{"key":"7471_CR42","doi-asserted-by":"publisher","unstructured":"Golhar, M., Huang, L. & Durr, N. J. HalF-SAM: SAM-based Haustral Fold Detection in Colonoscopy with Debris Suppression and Temporal Consistency. In Proc. of MICCAI 2025, https:\/\/doi.org\/10.1007\/978-3-032-05114-1_29 (2025).","DOI":"10.1007\/978-3-032-05114-1_29"},{"key":"7471_CR43","doi-asserted-by":"publisher","unstructured":"Jin, W., Daher, R., Stoyanov, D. & Vasconcelos, F. A self-supervised approach for detecting the edges of haustral folds in colonoscopy video. In Proc. MICCAI Workshop DEMI, 56\u201366, https:\/\/doi.org\/10.1007\/978-3-031-44992-5_6 (Springer, 2023).","DOI":"10.1007\/978-3-031-44992-5_6"},{"key":"7471_CR44","doi-asserted-by":"publisher","unstructured":"Golhar, M. V., Bobrow, T. L., Ngamruengphong, S. & Durr, N. J. GAN inversion for data augmentation to improve colonoscopy lesion classification. IEEE J. Biomed. Health Inform., https:\/\/doi.org\/10.1109\/JBHI.2024.3397611 (2024).","DOI":"10.1109\/JBHI.2024.3397611"},{"key":"7471_CR45","doi-asserted-by":"publisher","unstructured":"Psychogyios, D., Vasconcelos, F. & Stoyanov, D. Realistic endoscopic illumination modeling for nerf-based data generation. In Proc. MICCAI 2023, 535-544, https:\/\/doi.org\/10.1007\/978-3-031-43996-4_51 (Springer, 2023).","DOI":"10.1007\/978-3-031-43996-4_51"},{"key":"7471_CR46","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1109\/ACCESS.2020.3047544","volume":"9","author":"M Golhar","year":"2020","unstructured":"Golhar, M. et al. Improving colonoscopy lesion classification using semi-supervised deep learning. IEEE Access 9, 631\u2013640, https:\/\/doi.org\/10.1109\/ACCESS.2020.3047544 (2020).","journal-title":"IEEE Access"},{"key":"7471_CR47","doi-asserted-by":"publisher","unstructured":"Cold, K. M. et al. Development and validation of the open-source automatic bowel preparation scale. Gastrointest. Endosc. https:\/\/doi.org\/10.1016\/j.gie.2024.11.022 (2024).","DOI":"10.1016\/j.gie.2024.11.022"},{"key":"7471_CR48","doi-asserted-by":"crossref","unstructured":"Sharma, V., Jha, D., Bhuyan, M., Das, P. K. & Bagci, U. Diverse image generation with diffusion models and cross class label learning for polyp classification. Preprint at, https:\/\/arxiv.org\/abs\/2502.05444 (2025).","DOI":"10.1038\/s41598-026-49457-4"},{"key":"7471_CR49","doi-asserted-by":"publisher","first-page":"S3","DOI":"10.1016\/S0016-5107(03)02159-X","volume":"58","author":"R Lambert","year":"2003","unstructured":"Lambert, R. The paris endoscopic classification of superficial neoplastic lesions: esophagus, stomach, and colon: November 30 to december 1, 2002. Gastrointest. Endosc. 58, S3\u2013S43, https:\/\/doi.org\/10.1016\/S0016-5107(03)02159-X (2003).","journal-title":"Gastrointest. Endosc."},{"key":"7471_CR50","doi-asserted-by":"publisher","unstructured":"Ayers, F., Grant, A., Kuo, D., Cuccia, D. J. & Durkin, A. J. Fabrication and characterization of silicone-based tissue phantoms with tunable optical properties in the visible and near infrared domain. In Proc. SPIE 6870, 56\u201364, https:\/\/doi.org\/10.1117\/12.764969 (2008).","DOI":"10.1117\/12.764969"},{"key":"7471_CR51","doi-asserted-by":"publisher","first-page":"e201900005","DOI":"10.1002\/jbio.201900005","volume":"12","author":"JA Sweer","year":"2019","unstructured":"Sweer, J. A., Chen, M. T., Salimian, K. J., Battafarano, R. J. & Durr, N. J. Wide-field optical property mapping and structured light imaging of the esophagus with spatial frequency domain imaging. J. Biophotonics 12, e201900005, https:\/\/doi.org\/10.1002\/jbio.201900005 (2019).","journal-title":"J. Biophotonics"},{"key":"7471_CR52","doi-asserted-by":"publisher","first-page":"1988","DOI":"10.1109\/TMI.2019.2962786","volume":"39","author":"MT Chen","year":"2019","unstructured":"Chen, M. T., Mahmood, F., Sweer, J. A. & Durr, N. J. Ganpop: generative adversarial network prediction of optical properties from single snapshot wide-field images. IEEE Trans. Med. Imaging 39, 1988\u20131999, https:\/\/doi.org\/10.1109\/TMI.2019.2962786 (2019).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"7471_CR53","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1016\/j.vgie.2023.08.008","volume":"8","author":"AY Zhao","year":"2023","unstructured":"Zhao, A. Y. et al. How to make cost-effective polyp simulators for high-fidelity simulation-based training in postpolypectomy bleeding management and emr. VideoGIE 8, 520\u2013526, https:\/\/doi.org\/10.1016\/j.vgie.2023.08.008 (2023).","journal-title":"VideoGIE"},{"key":"7471_CR54","doi-asserted-by":"publisher","first-page":"739","DOI":"10.1038\/ajg.2009.104","volume":"104","author":"DK Rex","year":"2009","unstructured":"Rex, D. K. et al. American college of gastroenterology guidelines for colorectal cancer screening 2008. Am. J. Gastroenterol. 104, 739\u2013750, https:\/\/doi.org\/10.1038\/ajg.2009.104 (2009).","journal-title":"Am. J. Gastroenterol."},{"key":"7471_CR55","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1080\/10867651.2004.10487596","volume":"9","author":"A Telea","year":"2004","unstructured":"Telea, A. An image inpainting technique based on the fast marching method. J. Graph. Tools 9, 23\u201334, https:\/\/doi.org\/10.1080\/10867651.2004.10487596 (2004).","journal-title":"J. Graph. Tools"},{"key":"7471_CR56","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1016\/B978-0-12-336156-1.50061-6","volume":"4","author":"KJ Zuiderveld","year":"1994","unstructured":"Zuiderveld, K. J. et al. Contrast limited adaptive histogram equalization. Graphics Gems 4, 474\u2013485, https:\/\/doi.org\/10.1016\/B978-0-12-336156-1.50061-6 (1994).","journal-title":"Graphics Gems"},{"key":"7471_CR57","doi-asserted-by":"publisher","unstructured":"Soria, X., Riba, E. & Sappa, A. Dense extreme inception network: towards a robust cnn model for edge detection. In Proc. WACV 2020, 1912\u20131921, https:\/\/doi.org\/10.1109\/WACV45572.2020.9093290 (2020).","DOI":"10.1109\/WACV45572.2020.9093290"},{"key":"7471_CR58","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","volume":"8","author":"J Canny","year":"1986","unstructured":"Canny, J. A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 8, 679\u2013698, https:\/\/doi.org\/10.1109\/TPAMI.1986.4767851 (1986).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"7471_CR59","doi-asserted-by":"publisher","first-page":"717","DOI":"10.1109\/70.326576","volume":"10","author":"FC Park","year":"1994","unstructured":"Park, F. C. & Martin, B. J. Robot sensor calibration: solving AX = XB on the euclidean group. IEEE Trans. Robot. Autom. 10, 717\u2013721, https:\/\/doi.org\/10.1109\/70.326576 (1994).","journal-title":"IEEE Trans. Robot. Autom."},{"key":"7471_CR60","doi-asserted-by":"publisher","unstructured":"Scaramuzza, D., Martinelli, A. & Siegwart, R. A toolbox for easily calibrating omnidirectional cameras. In Proc. IROS 2006, 5695\u20135701, https:\/\/doi.org\/10.1109\/IROS.2006.282372 (IEEE, 2006).","DOI":"10.1109\/IROS.2006.282372"},{"key":"7471_CR61","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1162\/106365603321828970","volume":"11","author":"N Hansen","year":"2003","unstructured":"Hansen, N., Muller, S. D. & Koumoutsakos, P. Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (cma-es). Evol. Comput. 11, 1\u201318, https:\/\/doi.org\/10.1162\/106365603321828970 (2003).","journal-title":"Evol. Comput."},{"key":"7471_CR62","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","volume":"30","author":"A Fedorov","year":"2012","unstructured":"Fedorov, A. et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn. Reson. Imaging 30, 1323\u20131341, https:\/\/doi.org\/10.1016\/j.mri.2012.05.001 (2012).","journal-title":"Magn. Reson. Imaging"},{"key":"7471_CR63","doi-asserted-by":"publisher","DOI":"10.1155\/2010\/814319","volume":"2010","author":"M Arnold","year":"2010","unstructured":"Arnold, M., Ghosh, A., Ameling, S. & Lacey, G. Automatic segmentation and inpainting of specular highlights for endoscopic imaging. EURASIP J. Image Video Process. 2010, 814319, https:\/\/doi.org\/10.1155\/2010\/814319 (2010).","journal-title":"EURASIP J. Image Video Process."},{"key":"7471_CR64","unstructured":"Tian, Q. et al. Endoomni: zero-shot cross-dataset depth estimation in endoscopy by robust self-learning from noisy labels. Preprint at https:\/\/arxiv.org\/abs\/2409.05442 (2024)."},{"key":"7471_CR65","doi-asserted-by":"publisher","unstructured":"Golhar, M., Iwahori, Y., Bhuyan, M. K., Funahashi, K. & Kasugai, K. Blood vessel delineation in endoscopic images with deep learning based scene classification. In Pattern Recognition Applications and Methods, 147\u2013168, https:\/\/doi.org\/10.1007\/978-3-319-93647-5_9 (Springer, 2018).","DOI":"10.1007\/978-3-319-93647-5_9"},{"key":"7471_CR66","doi-asserted-by":"publisher","unstructured":"Golhar, M., Iwahori, Y., Bhuyan, M. K., Funahashi, K. & Kasugai, K. A robust method for blood vessel extraction in endoscopic images with svm-based scene classification. In Proc. ICPRAM, 148\u2013156, https:\/\/doi.org\/10.5220\/0006192601480156 (2017).","DOI":"10.5220\/0006192601480156"},{"key":"7471_CR67","doi-asserted-by":"publisher","unstructured":"Fulton, M. J., Prendergast, J. M., DiTommaso, E. R. & Rentschler, M. E. Comparing visual odometry systems in actively deforming simulated colon environments. In Proc. IROS 2020, 4988\u20134995, https:\/\/doi.org\/10.1109\/IROS45743.2020.9341159 (IEEE, 2020).","DOI":"10.1109\/IROS45743.2020.9341159"}],"container-title":["Scientific Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07471-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07471-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07471-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T22:41:38Z","timestamp":1788388898000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41597-026-07471-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,3]]},"references-count":67,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["7471"],"URL":"https:\/\/doi.org\/10.1038\/s41597-026-07471-1","relation":{},"ISSN":["2052-4463"],"issn-type":[{"value":"2052-4463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,3]]},"assertion":[{"value":"19 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"V.S.A. is the co-founder of Origin Endoscopy Inc., Sotelix Endoscopy Inc., Flow Therapeutics Inc. and Solv Endotherapy Inc., and a consultant for Dragonfly Endoscopy, Ionis Pharmacy, and Olympus Medical. V.S.A has educational grants from Boston Scientific and Medtronic, and research grants from Nestle and Pentax Medical. All other authors have no relevant competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Ethics"}}],"article-number":"1273"}}