Abstract
A colonoscopy is a medical examination used to check disease or abnormalities in the large intestine. If necessary, polyps or adenomas would be removed through the scope during a colonoscopy. Colorectal cancer can be prevented through this. However, the polyp detection rate differs depending on the condition and skill level of the endoscopist. Even some endoscopists have a 90% chance of missing an adenoma. Artificial intelligence and robot technologies for colonoscopy are being studied to compensate for these problems. In this study, we propose a self-supervised monocular depth estimation using spatiotemporal consistency in the colon environment. It is our contribution to propose a loss function for reconstruction errors between adjacent predicted depths and a depth feedback network that uses predicted depth information of the previous frame to predict the depth of the next frame. We performed quantitative and qualitative evaluation of our approach, and the proposed FBNet (depth FeedBack Network) outperformed state-of-the-art results for unsupervised depth estimation on the UCL datasets.
Highlights
In order to improve the performance of monocular depth estimation, we propose a depth reconstruction loss that compares the similarity between the warped previous depth and the current depth
Are used for quantitative evaluation of the self-supervised monocular depth estimation proposed in this work
The evaluation of the performance improvement to the depth feedback network used for depth estimation loss of colonoscopy images
Summary
According to Global Cancer Statistics 2018 [1], colorectal cancer causes approximately. 90,000 deaths worldwide each year, with the highest incidence rates in Europe, Australia, New Zealand, North America, and Asia. Colonoscopy is a test for the detection and removal of polyps, and it can prevent cancer by detecting adenoma. The polyp detection rate varies according to the condition and skill level of the endoscopist, and even some endoscopists have a 90% chance of missing an adenoma [2]. Endoscopy doctors’ fatigue and skill problems can be compensated for by artificial intelligence and robotic medical systems [3]. Polyp detection [4], size classification [5], and detecting deficient coverage in colonoscopy [6] have been proposed as computer-assisted technologies using artificial intelligence. In the field of robotic colonoscopy technology, there are studies on conventional colonoscope miniaturizing [3], robotic meshworm [7], treaded capsule [8], and autonomous locomotion system [9] to facilitate colonoscopy
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