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

TL;DR

This paper introduces ToDER, a novel deep learning pipeline with geometry constraints for accurate colonoscopy depth estimation and reconstruction, improving visualization and diagnosis in medical procedures.

Contribution

We propose ToDER, a bi-directional adaptation architecture with a TNet module that incorporates geometry constraints for precise depth estimation in colonoscopy videos.

Findings

Outperforms existing self-supervised and domain adaptation methods in depth prediction accuracy.

Effectively visualizes unobserved colon regions, aiding diagnosis.

Demonstrates potential for improved colonoscopy visualization and diagnosis.

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.