准确和快速的单眼内镜深度估计结构内容综合扩散.
Min Tan1, Yushun Tao1, Boyun Zheng2
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China; University of Chinese Academy of Sciences, Beijing, 101400, China.
概括
我们开发了结构-内容集成扩散估计 (SCIDE) 以准确的内镜深度估计. 这种方法在微创手术中显著提高了实时性能.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
背景情况:
- 内镜深度估计对于手术导航和3D重建至关重要.
- 目前的单眼深度估计方法在具有挑战性的内镜条件下失败,例如灯光不佳和对比度低.
- 不准确的深度预测阻碍了实时手术应用.
研究的目的:
- 开发一种准确和快速的内镜深度估计方法.
- 在复杂的外科环境中解决现有方法的局限性.
- 为了实现微创手术的实时深度估计.
主要方法:
- 拟议的结构-内容综合扩散估计 (SCIDE) 框架.
- 引入结构内容提取器 (SC-Extractor) 用于内镜预提取.
- 开发了快速优化扩散采样器 (FODS) 以实现高效的扩散模型采样.
主要成果:
- 斯基德实现了0.0875.5的低RMSE.
- FODS将推断时间减少了74.2%,使实时应用成为可能.
- 在内镜深度估计中展示了最先进的准确性.
结论:
- SCIDE提供了准确和快速的内镜深度估计.
- SCIDE框架使实时应用在内镜手术中成为可能.
- 这一进步支持改进的外科导航和机器人辅助.
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