Purpose

Robot-assisted surgery at the temporal bone utilizing a flexible drilling unit would allow safer access to clinical targets such as the cochlea or the internal auditory canal by navigating along nonlinear trajectories. One key sub-step for clinical realization of such a procedure is automated preoperative surgical planning that incorporates both segmentation of risk structures and optimized trajectory planning.

Methods

We automatically segment risk structures using 3D U-Nets with probabilistic active shape models. For nonlinear trajectory planning, we adapt bidirectional rapidly exploring random trees on Bézier Splines followed by sequential convex optimization. Functional evaluation, assessing segmentation quality based on the subsequent trajectory planning step, shows the suitability of our novel segmentation approach for this two-step preoperative pipeline.

Results

Based on 24 data sets of the temporal bone, we perform a functional evaluation of preoperative surgical planning. Our experiments show that the automated segmentation provides safe and coherent surface models that can be used in collision detection during motion planning. The source code of the algorithms will be made publicly available.

Conclusion

Optimized trajectory planning based on shape regularized segmentation leads to safe access canals for temporal bone surgery. Functional evaluation shows the promising results for both 3D U-Net and Bézier Spline trajectories.

中文翻译：

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颞骨手术优化术前计划的回顾性计算机评估

目的

利用灵活的钻孔装置在颞骨上进行机器人辅助手术将允许通过沿着非线性轨迹导航更安全地进入耳蜗或内耳道等临床目标。这种手术临床实现的一个关键子步骤是自动化术前手术规划，它结合了风险结构的分割和优化的轨迹规划。

方法

我们使用具有概率活动形状模型的 3D U-Nets 自动分割风险结构。对于非线性轨迹规划，我们在 Bézier Splines 上采用双向快速探索随机树，然后是顺序凸优化。基于随后的轨迹规划步骤评估分割质量的功能评估显示了我们的新型分割方法对于这种两步术前管道的适用性。

结果

基于 24 个颞骨数据集，我们对术前手术计划进行了功能评估。我们的实验表明，自动分割提供了安全且连贯的表面模型，可用于运动规划期间的碰撞检测。算法的源代码将公开。

结论

基于形状正则化分割的优化轨迹规划导致颞骨手术的安全通路。功能评估显示了 3D U-Net 和 Bézier Spline 轨迹的有希望的结果。