Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
Med Image Anal. 2019 Feb;52:1-12. doi: 10.1016/j.media.2018.11.005. Epub 2018 Nov 13.
Cochlear implants (CIs) are neural prosthetics that provide a sense of sound to people who experience severe to profound hearing loss. Recent studies have demonstrated a correlation between hearing outcomes and intra-cochlear locations of CI electrodes. Our group has been conducting investigations on this correlation and has been developing an image-guided cochlear implant programming (IGCIP) system to program CI devices to improve hearing outcomes. One crucial step that has not been automated in IGCIP is the localization of CI electrodes in clinical CTs. Existing methods for CI electrode localization do not generalize well on large-scale datasets of clinical CTs implanted with different brands of CI arrays. In this paper, we propose a novel method for localizing different brands of CI electrodes in clinical CTs. We firstly generate the candidate electrode positions at sub-voxel resolution in a whole head CT by thresholding an up-sampled feature image and voxel-thinning the result. Then, we use a graph-based path-finding algorithm to find a fixed-length path that consists of a subset of the candidates as the localization result. Validation on a large-scale dataset of clinical CTs shows that our proposed method outperforms the state-of-art CI electrode localization methods and achieves a mean error of 0.12 mm when compared to expert manual localization results. This represents a crucial step in translating IGCIP from the laboratory to large-scale clinical use.
人工耳蜗（Cochlear implants，CIs）是一种神经假体，可以为患有重度至极重度听力损失的人提供声音感知。最近的研究表明，CIs 电极在耳蜗内的位置与听力结果之间存在相关性。我们的团队一直在研究这种相关性，并开发了一种图像引导的人工耳蜗编程（Image-guided cochlear implant programming，IGCIP）系统，以编程人工耳蜗设备来改善听力结果。在 IGCIP 中，有一个关键步骤尚未实现自动化，即临床 CT 中 CIs 电极的定位。现有的 CIs 电极定位方法在植入不同品牌 CIs 数组的大规模临床 CT 数据集上无法很好地推广。在本文中，我们提出了一种在临床 CT 中定位不同品牌 CIs 电极的新方法。我们首先通过对上采样的特征图像进行阈值处理和体素细化，在整个头部 CT 中以亚像素分辨率生成候选电极位置。然后，我们使用基于图的路径查找算法找到由候选电极子集组成的固定长度路径作为定位结果。在大规模临床 CT 数据集上的验证表明，我们提出的方法优于现有的 CIs 电极定位方法，与专家手动定位结果相比，平均误差为 0.12 毫米。这是将 IGCIP 从实验室转化为大规模临床应用的关键步骤。
