ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation
<|reference_start|>ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation: 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.<|reference_end|>
@article{wu2024toder:,
    title={ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with
  Geometry Constraint Adaptation},
    author={Zhenhua Wu, Yanlin Jin, Liangdong Qiu, Xiaoguang Han, Xiang Wan,
  Guanbin Li},
    journal={arXiv preprint arXiv:2407.16508},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.16508},
    primaryClass={cs.CV}
}