小儿 CT 扫描中面神经和鼓索神经的自动分割。
Automatic segmentation of the facial nerve and chorda tympani in pediatric CT scans.
机构信息
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
出版信息
Med Phys. 2011 Oct;38(10):5590-600. doi: 10.1118/1.3634048.
PURPOSE
Cochlear 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.
METHODS
The 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.
RESULTS
A 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.
CONCLUSIONS
The 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.
目的
人工耳蜗植入术用于将电极阵列植入耳蜗以治疗听力损失。作者最近引入了一种微创的图像引导技术，称为经皮耳蜗植入术。该方法通过从外颅骨穿过面神经和鼓索之间的面部隐窝，在单一线性通道中钻孔进入耳蜗来实现进入耳蜗的通道。为了利用现有的计算自动安全钻孔轨迹的方法，需要对面神经和鼓索进行分割。这项工作的目的是自动分割小儿 CT 扫描中的面神经和鼓索。
方法
作者提出了一种自动技术，以实现成人患者的分割任务，该技术依赖于结构的统计模型。这些模型包含这两个结构的中心轴上的强度和形状信息。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，作者了解到儿童解剖结构与成人之间存在很大差异，当使用成人模型对儿科体积进行分割时，会导致分割结果不佳。因此，作者为儿科病例建立了一个新的模型，并使用它来分割儿科扫描。一旦建立了这个新模型，作者就使用相同的分割方法对成年人进行分割，并使用针对儿科解剖结构优化的算法参数。
结果
在 10 个 CT 扫描的验证实验中，手动分割的结构与自动分割的结构进行了比较。平均、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 毫米。
结论
结果表明，小儿扫描中面神经和鼓索的精确分割是可行的，这表明也可以自动计算安全的钻孔轨迹。
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