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Endoscopic Studies I: Bronchoscopy and Thoracoscopy

Endoscopic Studies I: Bronchoscopy and Thoracoscopy

Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
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: Mar 2, 2026

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

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Deep monocular 3D reconstruction for assisted navigation in bronchoscopy.

Marco Visentini-Scarzanella¹, Takamasa Sugiura², Toshimitsu Kaneko²

¹Multimedia Laboratory, Toshiba Corporate Research and Development Center, 1, Komukai-Toshiba-cho, Kawasaki, 212-8582, Japan. marco.visentiniscarzanella@gmail.com.

International Journal of Computer Assisted Radiology and Surgery

|May 17, 2017

Summary

This summary is machine-generated.

This study introduces a novel deep learning approach for real-time bronchoscopy navigation, improving depth estimation accuracy by 60% for precise lesion targeting during biopsies.

Keywords:

3D reconstruction Assisted navigation Bronchoscopy Deep learning

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Area of Science:

Medical Imaging
Computer Vision
Deep Learning

Background:

Bronchoscopy navigation for peripheral lesion biopsy requires accurate guidance.
Computer vision offers a low-cost solution for navigation assistance.

Purpose of the Study:

To propose a decoupled deep learning architecture for projecting bronchoscopy frames onto CT renderings.
To enable offline training using patient-specific CT data for improved navigation.

Main Methods:

Implemented a fully convolutional network on GPU.
Tested on a phantom dataset with 32 video sequences and ~60k frames.
Developed the first public dataset for bronchoscopy navigation.

Main Results:

Achieved an average depth accuracy of 1.5 mm, a 60% improvement over conventional methods.
Demonstrated a computational time of ~30 ms on modern GPUs.
Qualitative results showed close resemblance between estimated depth/renderings and ground truth.

Conclusions:

The novel architecture enables real-time monocular depth estimation in bronchoscopy.
The method maintains patient specificity without losing accuracy.
Future work includes integration into SLAM systems and in vivo data collection.