手术增强现实注册方法:从传统到深度学习方法的审查
Alberto Neri1, Veronica Penza2, Chiara Baldini1
1Biomedical Robotics Lab, Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy; Department of Computer Science, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy.
概括
增强现实 (AR) 通过重叠关键数据来提高手术精度. 本综述探讨了腹腔镜AR的传统和深度学习 (DL) 注册方法,评估了它们的优缺点.
科学领域:
- 计算机视觉 计算机视觉
- 医疗技术 医疗技术 医学技术
- 手术导航 手术导航
背景情况:
- 增强现实 (AR) 在研究中越来越重要,特别是在外科手术中.
- AR系统将关键信息叠加到外科医生的视野中,改善情境意识和外科结果.
研究的目的:
- 审查腹腔镜增强现实 (AR) 的注册方法.
- 分析基于传统和深度学习 (DL) 的方法,将手术前的3D模型与手术内视频数据对齐.
- 讨论这些方法在刚性和非刚性手术场景中的优缺点.
主要方法:
- 分析没有使用深度学习 (DL) 的传统注册技术.
- 研究混合DL增强方法,将DL整合到传统管道中.
- 检查端到端DL点云注册方法用于直接转换矩阵估计.
主要成果:
- 传统方法为外科AR注册提供了基线.
- 混合DL方法利用DL来提高既定技术的性能.
- DL点云注册为转换估计提供端到端解决方案.
结论:
- 无论是传统的还是基于DL的注册方法,对外科AR都有明显的优势和局限性.
- 了解这些方法对于推进腹腔镜中AR应用至关重要.
- 需要进一步的研究,以优化DL方法复杂的,非刚性外科环境.
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