SimCol3D -- 3D Reconstruction during Colonoscopy Challenge
Anita Rau Sophia Bano Yueming Jin Pablo Azagra Javier Morlana Edward Sanderson Bogdan J. Matuszewski Jae Young Lee Dong-Jae Lee Erez Posner Netanel Frank Varshini Elangovan Sista Raviteja Zhengwen Li Jiquan Liu Seenivasan Lalithkumar Mobarakol Islam Hongliang Ren José M. M. Montiel Danail Stoyanov
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 unsolved due to numerous factors such as self-occlusion, reflective
surfaces, lack of texture, and tissue deformation that limit feature-based
methods. Learning-based approaches hold promise as robust alternatives, but
necessitate extensive datasets. By establishing a benchmark, 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 in virtual colonoscopy is robustly solvable, while pose
estimation remains an open research question.