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.
@article{arxiv.2307.11261,
title = {SimCol3D -- 3D Reconstruction during Colonoscopy Challenge},
author = {Anita Rau and Sophia Bano and Yueming Jin and Pablo Azagra and Javier Morlana and Rawen Kader and Edward Sanderson and Bogdan J. Matuszewski and Jae Young Lee and Dong-Jae Lee and Erez Posner and Netanel Frank and Varshini Elangovan and Sista Raviteja and Zhengwen Li and Jiquan Liu and Seenivasan Lalithkumar and Mobarakol Islam and Hongliang Ren and Laurence B. Lovat and José M. M. Montiel and Danail Stoyanov},
journal= {arXiv preprint arXiv:2307.11261},
year = {2024}
}