ToDER: Towards Colonoscopy Depth Estimation and Reconstruction with Geometry Constraint Adaptation
This work addresses the challenge of visualizing unobserved regions in colonoscopies to prevent misdiagnoses, representing an incremental improvement over prior self-supervised and domain adaptation approaches.
The paper tackled the problem of inaccurate depth estimation in colonoscopy videos, which hinders reliable 3D reconstruction for medical visualization, and proposed ToDER, a bi-directional adaptation architecture with geometry constraints that achieved precise depth predictions in both realistic and synthetic videos compared to existing methods.
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.