LungDepth: Self-Supervised Multi-Frame Monocular Depth Estimation for Bronchoscopy

Abstract

Background

Bronchoscopy is an essential measure for conducting lung biopsies in clinical practice. It is crucial for advancing the intelligence of bronchoscopy to acquire depth information from bronchoscopic image sequences.

Methods

A self-supervised multi-frame monocular depth estimation approach for bronchoscopy is constructed. Networks are trained by minimising the photometric reprojection error between the target frame and the reconstructed target frame. The adaptive dual attention module and the details emphasis module are introduced to better capture the edge contour and internal details. In addition, the approach is evaluated on a self-made dataset and compared against other established methods.

Results

Experimental results demonstrate that the proposed method outperforms other self-supervised monocular depth estimation approaches in both quantitative measurement and qualitative analysis.

Conclusion

Our monocular depth estimation approach for bronchoscopy achieves superior performance in terms of error and accuracy, and passes physical model validations, which can facilitate further research into intelligent bronchoscopic procedures.