Mathew Alwyn, Magerand Ludovic, Trucco Emanuele, Manfredi Luigi
Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee, United Kingdom.
Discipline of Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom.
Front Robot AI. 2023 Jul 25;10:1212525. doi: 10.3389/frobt.2023.1212525. eCollection 2023.
Optical colonoscopy is the gold standard procedure to detect colorectal cancer, the fourth most common cancer in the United Kingdom. Up to 22%-28% of polyps can be missed during the procedure that is associated with interval cancer. A vision-based autonomous soft endorobot for colonoscopy can drastically improve the accuracy of the procedure by inspecting the colon more systematically with reduced discomfort. A three-dimensional understanding of the environment is essential for robot navigation and can also improve the adenoma detection rate. Monocular depth estimation with deep learning methods has progressed substantially, but collecting ground-truth depth maps remains a challenge as no 3D camera can be fitted to a standard colonoscope. This work addresses this issue by using a self-supervised monocular depth estimation model that directly learns depth from video sequences with view synthesis. In addition, our model accommodates wide field-of-view cameras typically used in colonoscopy and specific challenges such as deformable surfaces, specular lighting, non-Lambertian surfaces, and high occlusion. We performed qualitative analysis on a synthetic data set, a quantitative examination of the colonoscopy training model, and real colonoscopy videos in near real-time.
光学结肠镜检查是检测结直肠癌的金标准程序，结直肠癌是英国第四大常见癌症。在与间隔期癌症相关的检查过程中，高达22%-28%的息肉可能会被漏检。一种基于视觉的结肠镜自主软式内窥镜机器人可以通过更系统地检查结肠并减少不适，大幅提高检查的准确性。对环境的三维理解对于机器人导航至关重要，还可以提高腺瘤检测率。使用深度学习方法进行单目深度估计已经取得了显著进展，但由于无法将3D相机安装到标准结肠镜上，收集真实深度图仍然是一个挑战。这项工作通过使用一种自监督单目深度估计模型来解决这个问题，该模型通过视图合成直接从视频序列中学习深度。此外，我们的模型适用于结肠镜检查中通常使用的宽视野相机以及诸如可变形表面、镜面反射光、非朗伯表面和高遮挡等特定挑战。我们对一个合成数据集进行了定性分析，对结肠镜检查训练模型进行了定量检验，并对真实的结肠镜视频进行了近实时分析。
