一个基于cGAN的网络,用于从支气管镜图像进行深度估计
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
概括
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.
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.
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