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首页> 外文会议>Conference on image processing >An artifact-robust, shape library-based algorithm for automatic segmentation of inner ear anatomy in post-cochlear-implantation CT
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An artifact-robust, shape library-based algorithm for automatic segmentation of inner ear anatomy in post-cochlear-implantation CT

机译：基于人工形状鲁棒性的基于形状库的人工耳蜗植入后CT内耳解剖结构自动分割算法

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A cochlear implant (CI) is a device that restores hearing using an electrode array that is surgically placed in the cochlea. After implantation, the CI is programmed to attempt to optimize hearing outcome. Currently, we are testing an image-guided CI programming (IGCIP) technique we recently developed that relies on knowledge of relative position of intra-cochlear anatomy to implanted electrodes. IGCIP is enabled by a number of algorithms we developed that permit determining the positions of electrodes relative to intra-cochlear anatomy using a pre- and a post-implantation CT. One issue with this technique is that it cannot be used for many subjects for whom a pre-implantation CT was not acquired. Pre-implantation CT has been necessary because it is difficult to localize the intra-cochlear structures in post-implantation CTs alone due to the image artifacts that obscure the cochlea. In this work, we present an algorithm for automatically segmenting intra-cochlear anatomy in post-implantation CTs. Our approach is to first identify the labyrinth and then use its position as a landmark to localize the intra-cochlea anatomy. Specifically, we identify the labyrinth by first approximately estimating its position by mapping a labyrinth surface of another subject that is selected from a library of such surfaces and then refining this estimate by a standard shape model-based segmentation method. We tested our approach on 10 ears and achieved overall mean and maximum errors of 0.209 and 0.98 mm, respectively. This result suggests that our approach is accurate enough for developing IGCIP strategies based solely on post-implantation CTs.
机译：耳蜗植入物（CI）是一种通过外科手术放置在耳蜗中的电极阵列恢复听力的装置。植入后，对CI进行编程以尝试优化听力结果。当前，我们正在测试我们最近开发的图像引导CI编程（IGCIP）技术，该技术依赖于耳蜗内解剖结构与植入电极的相对位置的知识。 IGCIP由我们开发的多种算法支持，这些算法允许使用植入前和植入后CT确定电极相对于耳蜗内解剖结构的位置。该技术的一个问题是，它不能用于许多未获得植入前CT的受试者。植入前CT是必需的，因为由于遮盖耳蜗的图像伪影，很难仅在植入后CT中定位耳蜗内结构。在这项工作中，我们提出了一种在植入后的CT中自动分割耳蜗内解剖结构的算法。我们的方法是首先识别迷宫，然后将其位置用作界标以定位耳蜗内解剖结构。具体来说，我们首先通过映射另一个对象的迷宫表面（通过从此类表面的库中选择）来近似估计其位置，然后通过基于标准形状模型的分割方法完善此估算值，从而确定迷宫的位置。我们在10个耳朵上测试了我们的方法，并获得了0.209和0.98 mm的总体平均和最大误差。该结果表明，我们的方法对于仅基于植入后CT制定IGCIP策略足够准确。

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