135746 20260217205524.0 doi 10.1016/j.media.2024.103195 sideral 138764 ART-2024-138764 eng Rau, Anita SimCol3D — 3D reconstruction during colonoscopy challenge 2024 Access copy available to the general public Unrestricted Colorectal cancer is one of the most common cancers in the world. While colonoscopy is an effective screening technique, navigating an endoscope through the colon to detect polyps is challenging. A 3D map of the observed surfaces could enhance the identification of unscreened colon tissue and serve as a training platform. However, reconstructing the colon from video footage remains difficult. Learning-based approaches hold promise as robust alternatives, but necessitate extensive datasets. Establishing a benchmark dataset, the 2022 EndoVis sub-challenge SimCol3D aimed to facilitate data-driven depth and pose prediction during colonoscopy. The challenge was hosted as part of MICCAI 2022 in Singapore. Six teams from around the world and representatives from academia and industry participated in the three sub-challenges: synthetic depth prediction, synthetic pose prediction, and real pose prediction. This paper describes the challenge, the submitted methods, and their results. We show that depth prediction from synthetic colonoscopy images is robustly solvable, while pose estimation remains an open research question. info:eu-repo/grantAgreement/EC/H2020/863146/EU/EndoMapper: Real-time mapping from endoscopic video/EndoMapper This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 863146-EndoMapper info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es 11.8 2024 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 13 / 204 = 0.064 2024 Q1 T1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING 4 / 213 = 0.019 2024 Q1 T1 ENGINEERING, BIOMEDICAL 7 / 124 = 0.056 2024 Q1 T1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS 5 / 177 = 0.028 2024 Q1 T1 3.289 2024 Computer Graphics and Computer-Aided Design 2024 Q1 Computer Vision and Pattern Recognition 2024 Q1 Radiology, Nuclear Medicine and Imaging 2024 Q1 Radiological and Ultrasound Technology 2024 Q1 Health Informatics 2024 Q1 26.6 2024 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Bano, Sophia Jin, Yueming Azagra, Pablo (orcid)0000-0002-3567-3294 Morlana, Javier Universidad de Zaragoza Kader, Rawen Sanderson, Edward Matuszewski, Bogdan J. Lee, Jae Young Lee, Dong-Jae Posner, Erez Frank, Netanel Elangovan, Varshini Raviteja, Sista Li, Zhengwen Liu, Jiquan Lalithkumar, Seenivasan Islam, Mobarakol Ren, Hongliang Lovat, Laurence B. Montiel, José M. M. Stoyanov, Danail 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 96 (2024), 103195 [16 pp.] Med. image anal. MEDICAL IMAGE ANALYSIS 1361-8415 5093061 http://zaguan.unizar.es/record/135746/files/texto_completo.pdf Versión publicada 2237606 http://zaguan.unizar.es/record/135746/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:135746 articulos driver 2026-02-17-20:30:02 ARTICLE