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相关概念视频

Endoscopic Studies I: Bronchoscopy and Thoracoscopy01:30

Endoscopic Studies I: Bronchoscopy and Thoracoscopy

234
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...
234
Endoscopic Studies II: Thoracocentesis01:26

Endoscopic Studies II: Thoracocentesis

333
Thoracentesis(Thoracocentesis), commonly known as pleural tap, is a medical procedure where a 22 gauge needle is inserted into the pleural space, the area between the lung and chest wall. This procedure is commonly performed to diagnose or treat various respiratory disorders.
Description
Excess pleural fluid or air may accumulate in some respiratory disorders in the thoracic cavity. To treat pleural effusion, a physician conducts thoracentesis by carefully piercing the chest wall and entering...
333

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相关实验视频

Updated: Jul 19, 2025

Author Spotlight: Expanding Interventional Pulmonology Research with Robotic-Assisted Bronchoscopy
04:10

Author Spotlight: Expanding Interventional Pulmonology Research with Robotic-Assisted Bronchoscopy

Published on: July 19, 2024

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一个基于cGAN的网络,用于从支气管镜图像进行深度估计.

Lu Guo1, Werner Nahm2

  • 1Institute of Biomedical Engineering, Karlsruhe Institute of Technology, Kaiserstraße 12, 76131, Karlsruhe, Germany. publications@ibt.kit.edu.

International journal of computer assisted radiology and surgery
|August 10, 2023
PubMed
概括
此摘要是机器生成的。

这项研究介绍了BronchoDep-GAN,这是一种用于从支气管镜图像中改进深度估计的新型网络. 使用合成数据和真实数据进行训练可以增强3D重建,以便更好地进行支气管镜导航.

关键词:
支气管镜检查 (bronchoscopy) 是一种用于检查支气管的方法.有条件的GANs可以使用.深度估计估计的深度.图像引导手术是指导图像的手术.

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科学领域:

  • 医疗成像医学成像
  • 计算机视觉 计算机视觉
  • 手术导航 手术导航

背景情况:

  • 从二维支气管镜图像中精确的3D重建气道结构对于开发基于视觉的导航系统至关重要.
  • 深度估计是这个3D重建的基本步骤.

研究的目的:

  • 为了提高直接从支气管镜图像的深度估计性能.
  • 训练使用合成和真实数据集的深度估计网络.

主要方法:

  • 开发了一个基于条件生成对抗网络 (cGAN) 的网络,即BronchoDep-GAN.
  • 该网络将支气管镜图像转化为深度图.
  • 培训包括对合成和虚拟数据进行监督学习,以及对未配对的真实数据进行无监督学习.

主要成果:

  • 与已建立的pix2pix cGAN相比,BronchoDep-GAN在深度估计方面表现出更高的准确性.
  • 在合成和真实支气管镜数据集上观察到性能改善.
  • 实时数据测试是定性测试,因为没有地面真相.

结论:

  • 在训练期间整合虚拟和真实支气管镜图像可以显著改善深度估计.
  • 未来的工作包括对3D临床幻影的验证和评估支气管镜局部化的准确性.
  • 这些发现支持了基于视觉的支气管导航系统的进步.