SimCol3D -- 3D Reconstruction during Colonoscopy Challenge
This addresses the challenge of navigating endoscopes for colorectal cancer screening by providing a training platform, though it's incremental as it builds on existing learning-based approaches.
The paper describes the SimCol3D challenge which established a benchmark dataset for 3D reconstruction during colonoscopy, showing that depth prediction from synthetic images is robustly solvable while pose estimation remains an open problem.
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.