Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans.
Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans.
复制标题
儿科 CT 扫描中面神经和鼓索的自动分割。
DOI：
10.1118/1.3634048
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发表时间：
2011
期刊：
影响因子：
3.8
通讯作者：
Dawant,BenoitM
中科院分区：
文献类型：
--
作者：
Reda,FitsumA;Noble,JackH;Rivas,Alejandro;McRackan,TheodoreR;Labadie,RobertF;Dawant,BenoitM
目的人工耳蜗植入手术用于在耳蜗中植入电极阵列以治疗听力损失。作者最近介绍了一种称为经皮耳蜗植入术的微创图像引导技术。这种方法通过从外颅骨经由面神经隐窝（面神经和鼓索所界定的区域）钻一个单一的线性通道进入耳蜗来实现对耳蜗的访问。为了利用现有的方法自动计算安全钻孔轨迹，面神经和鼓索需要被分割。这项工作的目标是自动分割面神经和鼓索在儿科CT scannes.MethodsThe作者提出了一种自动技术，以实现在成人患者的分割任务，依赖于统计模型的结构。这些模型包含沿两个结构的中心轴的强度和形状信息沿着。在这项工作中，作者试图使用相同的方法来分割儿科扫描中的结构。然而，作者了解到儿童和成人的解剖结构之间存在实质性差异，这导致在使用成人模型分割儿科体积时分割结果不佳。因此，作者为儿科病例构建了一个新模型，并将其用于分割儿科扫描。一旦这个新的模型建立，作者采用相同的分割方法，用于成人的算法参数进行了优化，为pediatric anatomy.ResultsA验证实验进行了10 CT扫描，手动分割的结构进行了比较，自动分割的结构。平均值，标准差，中位数，和最大分割误差分别为0.23，0.17，0.18，和1.27 mm，分别为.ConclusionsThe结果表明，准确的分割面神经和鼓索在儿科扫描是可以实现的，从而表明安全的钻孔轨迹也可以自动计算。
PurposeCochlear implant surgery is used to implant an electrode array in the cochlea to treat hearing loss. The authors recently introduced a minimally invasive image‐guided technique termed percutaneous cochlear implantation. This approach achieves access to the cochlea by drilling a single linear channel from the outer skull into the cochlea via the facial recess, a region bounded by the facial nerve and chorda tympani. To exploit existing methods for computing automatically safe drilling trajectories, the facial nerve and chorda tympani need to be segmented. The goal of this work is to automatically segment the facial nerve and chorda tympani in pediatric CT scans.MethodsThe authors have proposed an automatic technique to achieve the segmentation task in adult patients that relies on statistical models of the structures. These models contain intensity and shape information along the central axes of both structures. In this work, the authors attempted to use the same method to segment the structures in pediatric scans. However, the authors learned that substantial differences exist between the anatomy of children and that of adults, which led to poor segmentation results when an adult model is used to segment a pediatric volume. Therefore, the authors built a new model for pediatric cases and used it to segment pediatric scans. Once this new model was built, the authors employed the same segmentation method used for adults with algorithm parameters that were optimized for pediatric anatomy.ResultsA validation experiment was conducted on 10 CT scans in which manually segmented structures were compared to automatically segmented structures. The mean, standard deviation, median, and maximum segmentation errors were 0.23, 0.17, 0.18, and 1.27 mm, respectively.ConclusionsThe results indicate that accurate segmentation of the facial nerve and chorda tympani in pediatric scans is achievable, thus suggesting that safe drilling trajectories can also be computed automatically.