Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
IEEE Trans Biomed Eng. 2011 Sep;58(9):2625-32. doi: 10.1109/TBME.2011.2160262. Epub 2011 Jun 23.
Cochlear implant surgery is a procedure performed to treat profound hearing loss. Clinical results suggest that implanting the electrode in the scala tympani, one of the two principal cavities inside the cochlea, may result in better hearing restoration. Segmentation of intracochlear cavities could thus aid the surgeon to choose the point of entry and angle of approach that maximize the likelihood of successful implant insertion, which may lead to more substantial hearing restoration. However, because the membrane that separates the intracochlear cavities is too thin to be seen in conventional in vivo imaging, traditional segmentation techniques are inadequate. In this paper, we circumvent this problem by creating an active shape model with micro CT (μCT) scans of the cochlea acquired ex vivo. We then use this model to segment conventional CT scans. The model is fitted to the partial information available in the conventional scans and used to estimate the position of structures not visible in these images. Quantitative evaluation of our method, made possible by the set of μCTs, results in Dice similarity coefficients averaging 0.75. Mean and maximum surface errors average 0.21 and 0.80 mm.
人工耳蜗植入术是一种用于治疗深度听力损失的手术。临床结果表明，将电极植入耳蜗内的两个主要腔室之一的鼓阶内，可能会导致更好的听力恢复。因此，对内耳腔室的分割可以帮助外科医生选择最大程度提高植入成功可能性的进入点和进入角度，这可能会导致更实质性的听力恢复。然而，由于分隔内耳腔室的膜太薄，在传统的体内成像中无法看到，因此传统的分割技术是不够的。在本文中，我们通过创建一个使用离体获取的耳蜗微 CT（μCT）扫描的主动形状模型来解决这个问题。然后，我们使用这个模型来分割常规 CT 扫描。该模型适用于常规扫描中可用的部分信息，并用于估计这些图像中不可见结构的位置。通过一组 μCT 实现的我们方法的定量评估导致 Dice 相似系数平均为 0.75。平均和最大表面误差分别为 0.21 和 0.80 毫米。
