经常性多视图6DoF对没有标记器的外科工具跟踪进行估计
Niklas Agethen1, Janis Rosskamp2, Tom L Koller3,2
1Fraunhofer MEVIS, Max-von-Laue-Str. 2, 28359, Bremen, Germany. niklas.agethen@mevis.fraunhofer.de.
概括
这项研究引入了一种新的深度学习方法,用于使用多个RGB摄像头进行无标记手术仪器跟踪. 该方法提高了精度和可靠性,特别是在仪器封闭期间,为传统的基于标记器的系统提供了有竞争力的替代方案.
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
- 机器学习 机器学习
背景情况:
- 在外科导航中基于标记器的跟踪是精确的,但需要大量的准备,并且容易导致标记器封闭.
- 深度学习为使用RGB视频进行手术仪器跟踪提供了一个有前途的,不需要标记器的替代方案.
研究的目的:
- 应用对象姿势估计,使用一种新的深度学习架构来实现无标记手术仪器跟踪.
- 为了应对手术导航中耗时的准备和标记物阻塞的挑战.
主要方法:
- 结合多视图图形估计与循环神经网络 (RNN) 以利用时间连贯性.
- 将时空特征提取器集成到现有的姿势估计管道中,以进行基于序列的特征整合.
- 在仪表封闭条件下评估的性能.
主要成果:
- 在四摄像头设置的合成数据集上,平均尖端误差低于1.0毫米,角度误差低于0.2°.
- 在使用四个摄像头的真实数据集上,实现了低于3.0mm的错误.
- 与非反复方法相比,反复方法在有限的仪器可见性期间在尖端位置预测中显示了~3毫米的更高精度.
结论:
- 使用多个摄像头进行基于深度学习的跟踪显示了与手术仪器的基于标记器的系统的竞争力.
- 在仪器被封闭时,反复出现的时间信息显著提高了跟踪可靠性.
- 多视图处理和循环网络的结合提高了手术姿势估计的精度和可用性.
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