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Bronchoscopy
Description
Bronchoscopy is a procedure that involves direct visualization of the larynx, trachea, and bronchi for diagnostic and therapeutic purposes. A flexible fiber optic or rigid bronchoscope is used to carry out the procedure. The fiber-optic bronchoscope is more frequently used due...

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Related Experiment Video

Updated: May 29, 2025

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Published on: June 23, 2023

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

Jingsheng Xu¹, Bo Guan¹, Jianchang Zhao²

¹Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China.

The International Journal of Medical Robotics + Computer Assisted Surgery : MRCAS

|February 2, 2025

Summary

This summary is machine-generated.

This study introduces a new self-supervised monocular depth estimation method for bronchoscopy, significantly improving accuracy and facilitating intelligent procedures.

Keywords:

attention mechanism bronchoscopy monocular depth estimation self‐supervised learning

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Area of Science:

Medical Imaging
Computer Vision

Background:

Bronchoscopy is vital for lung biopsies.
Acquiring depth information from bronchoscopic images enhances procedural intelligence.

Purpose of the Study:

To develop a self-supervised monocular depth estimation approach for bronchoscopy.
To improve the accuracy and utility of depth perception in endoscopic procedures.

Main Methods:

A self-supervised multi-frame monocular depth estimation network was constructed.
Photometric reprojection error minimization was used for training.
Adaptive dual attention and details emphasis modules were incorporated to enhance feature capture.

Main Results:

The proposed method outperformed existing self-supervised monocular depth estimation techniques.
Superior quantitative and qualitative results were achieved on a custom dataset.

Conclusions:

The developed monocular depth estimation approach for bronchoscopy demonstrates high accuracy and reliability.
This advancement supports further research in intelligent bronchoscopic interventions.