Self-supervised monocular depth estimation for high field of view colonoscopy cameras

Frontiers in Robotics and AI

Robotics and AI

Ludovic Magerand¹  Emanuele Trucco¹  Luigi Manfredi²  Alwyn Mathew² 

[1] Discipline of Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom;Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee, United Kingdom;

关键词: colonoscopy;    depth estimation;    wide-angle camera;    endorobot;    navigation;   

DOI  :  10.3389/frobt.2023.1212525

 received in 2023-04-26, accepted in 2023-06-26,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

Unknown   
Copyright © 2023 Mathew, Magerand, Trucco and Manfredi.