000135746 001__ 135746 000135746 005__ 20250923084427.0 000135746 0247_ $$2doi$$a10.1016/j.media.2024.103195 000135746 0248_ $$2sideral$$a138764 000135746 037__ $$aART-2024-138764 000135746 041__ $$aeng 000135746 100__ $$aRau, Anita 000135746 245__ $$aSimCol3D — 3D reconstruction during colonoscopy challenge 000135746 260__ $$c2024 000135746 5060_ $$aAccess copy available to the general public$$fUnrestricted 000135746 5203_ $$aColorectal 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. 000135746 536__ $$9info:eu-repo/grantAgreement/EC/H2020/863146/EU/EndoMapper: Real-time mapping from endoscopic video/EndoMapper$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 863146-EndoMapper 000135746 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000135746 590__ $$a11.8$$b2024 000135746 592__ $$a3.289$$b2024 000135746 591__ $$aCOMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE$$b13 / 204 = 0.064$$c2024$$dQ1$$eT1 000135746 593__ $$aComputer Graphics and Computer-Aided Design$$c2024$$dQ1 000135746 591__ $$aRADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING$$b4 / 212 = 0.019$$c2024$$dQ1$$eT1 000135746 593__ $$aComputer Vision and Pattern Recognition$$c2024$$dQ1 000135746 591__ $$aENGINEERING, BIOMEDICAL$$b7 / 124 = 0.056$$c2024$$dQ1$$eT1 000135746 593__ $$aRadiology, Nuclear Medicine and Imaging$$c2024$$dQ1 000135746 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b5 / 175 = 0.029$$c2024$$dQ1$$eT1 000135746 593__ $$aRadiological and Ultrasound Technology$$c2024$$dQ1 000135746 593__ $$aHealth Informatics$$c2024$$dQ1 000135746 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000135746 700__ $$aBano, Sophia 000135746 700__ $$aJin, Yueming 000135746 700__ $$0(orcid)0000-0002-3567-3294$$aAzagra, Pablo 000135746 700__ $$aMorlana, Javier$$uUniversidad de Zaragoza 000135746 700__ $$aKader, Rawen 000135746 700__ $$aSanderson, Edward 000135746 700__ $$aMatuszewski, Bogdan J. 000135746 700__ $$aLee, Jae Young 000135746 700__ $$aLee, Dong-Jae 000135746 700__ $$aPosner, Erez 000135746 700__ $$aFrank, Netanel 000135746 700__ $$aElangovan, Varshini 000135746 700__ $$aRaviteja, Sista 000135746 700__ $$aLi, Zhengwen 000135746 700__ $$aLiu, Jiquan 000135746 700__ $$aLalithkumar, Seenivasan 000135746 700__ $$aIslam, Mobarakol 000135746 700__ $$aRen, Hongliang 000135746 700__ $$aLovat, Laurence B. 000135746 700__ $$aMontiel, José M. M. 000135746 700__ $$aStoyanov, Danail 000135746 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000135746 773__ $$g96 (2024), 103195 [16 pp.]$$pMed. image anal.$$tMEDICAL IMAGE ANALYSIS$$x1361-8415 000135746 8564_ $$s5093061$$uhttps://zaguan.unizar.es/record/135746/files/texto_completo.pdf$$yVersión publicada 000135746 8564_ $$s2237606$$uhttps://zaguan.unizar.es/record/135746/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000135746 909CO $$ooai:zaguan.unizar.es:135746$$particulos$$pdriver 000135746 951__ $$a2025-09-22-14:40:44 000135746 980__ $$aARTICLE