Automatic segmentation of intracochlear anatomy in conventional CT.
Automatic segmentation of intracochlear anatomy in conventional CT.
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DOI：
10.1109/tbme.2011.2160262
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发表时间：
2011-09
期刊：
影响因子：
--
通讯作者：
Dawant BM
中科院分区：
文献类型：
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作者：
Noble JH;Labadie RF;Majdani O;Dawant BM
人工耳蜗术是一种治疗严重听力损失的手术。临床结果表明，将电极植入耳蜗腔内的两个主要腔之一鼓阶，可能会导致更好的听力恢复。因此，耳蜗腔的分割可以帮助外科医生选择入口点和入路角度，从而最大限度地增加植入物成功植入的可能性，从而可能导致更实质性的听力恢复。然而，由于分隔耳蜗腔的膜太薄，在传统的活体成像中看不到，传统的分割技术是不够的。在这项工作中，我们通过创建一个具有微CT(μCT)扫描的体外获得的耳蜗组织的主动形状模型来绕过这个问题。然后，我们使用该模型对传统的CT扫描进行分割。该模型与常规扫描中可用的部分信息进行拟合，并用于估计在这些图像中不可见的结构的位置。通过一组μCT对我们的方法进行定量评估，得到的骰子相似系数平均为0.75。平均和最大表面误差分别为0.21和0.80 mm。
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 intra-cochlear 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 intra-cochlear cavities is too thin to be seen in conventional in vivo imaging, traditional segmentation techniques are inadequate. In this work, 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.