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.

翻译：结直肠癌是全球最常见的癌症之一。尽管结肠镜检查是一种有效的筛查技术，但通过内窥镜在结肠中导航以检测息肉仍具有挑战性。观察表面的3D地图可以增强对未筛查结肠组织的识别，并作为训练平台。然而，由于自遮挡、反光表面、缺乏纹理以及组织变形等因素限制了基于特征的方法，从视频片段中重建结肠的问题仍未解决。基于学习的方法作为稳健的替代方案具有前景，但需要大量数据集。通过建立基准，2022年EndoVis子挑战SimCol3D旨在促进结肠镜检查中数据驱动的深度和姿态预测。该挑战作为2022年新加坡MICCAI大会的一部分举办。来自世界各地的六个团队以及学术界和工业界的代表参与了三个子挑战：合成深度预测、合成姿态预测和真实姿态预测。本文描述了该挑战、提交的方法及其结果。我们表明，虚拟结肠镜检查中的深度预测可以稳健地解决，而姿态估计仍是一个开放的研究问题。