ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation

• URL: http://arxiv.org/abs/2407.16508v1
• Date: Tue, 23 Jul 2024 14:24:26 GMT
• Title: ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation
• Authors: Zhenhua Wu, Yanlin Jin, Liangdong Qiu, Xiaoguang Han, Xiang Wan, Guanbin Li,
• Abstract summary: We propose a novel reconstruction pipeline with a bi-directional adaptation architecture named ToDER to get precise depth estimations. Experimental results demonstrate that our approach can precisely predict depth maps in both realistic and synthetic colonoscopy videos.
• Score: 67.22294293695255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visualizing colonoscopy is crucial for medical auxiliary diagnosis to prevent undetected polyps in areas that are not fully observed. Traditional feature-based and depth-based reconstruction approaches usually end up with undesirable results due to incorrect point matching or imprecise depth estimation in realistic colonoscopy videos. Modern deep-based methods often require a sufficient number of ground truth samples, which are generally hard to obtain in optical colonoscopy. To address this issue, self-supervised and domain adaptation methods have been explored. However, these methods neglect geometry constraints and exhibit lower accuracy in predicting detailed depth. We thus propose a novel reconstruction pipeline with a bi-directional adaptation architecture named ToDER to get precise depth estimations. Furthermore, we carefully design a TNet module in our adaptation architecture to yield geometry constraints and obtain better depth quality. Estimated depth is finally utilized to reconstruct a reliable colon model for visualization. Experimental results demonstrate that our approach can precisely predict depth maps in both realistic and synthetic colonoscopy videos compared with other self-supervised and domain adaptation methods. Our method on realistic colonoscopy also shows the great potential for visualizing unobserved regions and preventing misdiagnoses.

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