Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors.
Automatic Neurocranial Landmarks Detection from Visible Facial Landmarks Leveraging 3D Head Priors.
复制标题
利用 3D 头部先验从可见面部标志自动检测神经颅标志。
DOI：
10.1007/978-3-031-45249-9_2
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
DiMartino,JMatias
中科院分区：
文献类型：
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作者：
Schlesinger,Oded;Kundu,Raj;Goetz,Stefan;Sapiro,Guillermo;Peterchev,AngelV;DiMartino,JMatias
定位和追踪神经颅标志在现代医疗程序中是必不可少的，例如经颅磁刺激（TMS）。然而，最先进的治疗仍然依赖于手动识别头部目标，并需要设置反向反射标记进行跟踪。这限制了TMS方法的适用性和可伸缩性，使它们非常耗时，依赖于昂贵的硬件，并且当反向反射标记偏离其初始位置时容易出错。为了克服这些限制，我们提出了一种可扩展的方法，能够推断头皮上感兴趣点的位置，例如，国际10-20系统的神经颅标志。与现有方法相比，我们的方法不需要人为干预或标记；头部标志估计利用可见的面部标志，可选的头部尺寸测量，和统计头部模型先验。我们在来自1,150名受试者的地面真实数据上验证了所提出的方法，其中面部3D和头部信息可用；我们的技术实现了平均2.56 mm的定位RMSE，与TMS中高端技术报道的相同数量级。我们的实现可以在https://github.com/odedsc/ANLD上获得。
The localization and tracking of neurocranial landmarks is essential in modern medical procedures, e.g., transcranial magnetic stimulation (TMS). However, state-of-the-art treatments still rely on the manual identification of head targets and require setting retroreflective markers for tracking. This limits the applicability and scalability of TMS approaches, making them time-consuming, dependent on expensive hardware, and prone to errors when retroreflective markers drift from their initial position. To overcome these limitations, we propose a scalable method capable of inferring the position of points of interest on the scalp, e.g., the International 10–20 System’s neurocranial landmarks. In contrast with existing approaches, our method does not require human intervention or markers; head landmarks are estimated leveraging visible facial landmarks, optional head size measurements, and statistical head model priors. We validate the proposed approach on ground truth data from 1,150 subjects, for which facial 3D and head information is available; our technique achieves a localization RMSE of 2.56 mm on average, which is of the same order as reported by high-end techniques in TMS. Our implementation is available at https://github.com/odedsc/ANLD.