从内镜图像进行3D重建:一项调查调查
Zhuoyue Yang1, Ju Dai2, Junjun Pan1
1State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, 37 Xueyuan Road, Haidian District, Beijing, 100191, China; Peng Cheng Lab, 2 Xingke 1st Street, Nanshan District, Shenzhen, Guangdong Province, 518000, China.
Computers in biology and medicine
|May 5, 2024
概括
本综述调查了内镜成像的3D重建方法,强调了深度学习.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 手术技术 手术技术
背景情况:
- 从内镜图像进行三维 (3D) 重建对于许多医学应用至关重要.
- 内镜被分为单眼或双眼,影响深度估计策略.
- 传统的深度估计方法与内镜挑战作斗争,例如光线不佳和纹理稀疏.
研究的目的:
- 综合审查和分类内镜成像深度估计方法.
- 分析传统和基于深度学习的方法的演变和性能.
- 为在临床环境中选择合适的方法提供指导.
主要方法:
- 从2013年到2023年,对170多篇论文进行了系统的文献审查.
- 基于内镜类型 (单眼/双眼) 的方法的分类.
- 分析传统的 (特征匹配,多视图几何) 和深度学习技术.
- 常见数据集,性能指标和场景表示的总结.
主要成果:
- 深度学习方法在克服传统技术在内镜环境中的局限性方面表现有前途.
- 介绍了方法的分类,它们的优点和缺点.
- 提供了对定性和定量性能,稳定性和处理时间的比较分析.
- 总结了内镜中常用的场景表示方法.
结论:
- 基于深度学习的深度估计正在快速推进内镜应用.
- 该审查有助于明智地选择医疗用途的3D重建方法.
- 讨论了医学成像中的深度估计的未来前景.
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