Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, 37235, USA.
Department of Otolaryngology - Head and Neck Surgery, Vanderbilt University, Nashville, TN, 37235, USA.
Med Phys. 2018 Nov;45(11):5030-5040. doi: 10.1002/mp.13185. Epub 2018 Oct 8.
Cochlear implants (CIs) are neural prosthetic devices that provide a sense of sound to people who experience profound hearing loss. Recent research has indicated that there is a significant correlation between hearing outcomes and the intracochlear locations of the electrodes. We have developed an image-guided cochlear implant programming (IGCIP) system based on this correlation to assist audiologists with programming CI devices. One crucial step in our IGCIP system is the localization of CI electrodes in postimplantation CTs. Existing methods for this step are either not fully automated or not robust. When the CI electrodes are closely spaced, it is more difficult to identify individual electrodes because there is no intensity contrast between them in a clinical CT. The goal of this work is to automatically segment the closely spaced CI electrode arrays in postimplantation clinical CTs.
The proposed method involves firstly identifying a bounding box that contains the cochlea by using a reference CT. Then, the intensity image and the vesselness response of the VOI are used to segment the regions of interest (ROIs) that may contain the electrode arrays. For each ROI, we apply a voxel thinning method to generate the medial axis line. We exhaustively search through all the possible connections of medial axis lines. For each possible connection, we define CI array centerline candidates by selecting two points on the connected medial axis lines as the array endpoints. For each CI array centerline candidate, we use a cost function to evaluate its quality, and the one with the lowest cost is selected as the array centerline. Then, we fit an a priori known geometric model of the array to the centerline to localize the individual electrodes. The method was trained on 28 clinical CTs of CI recipients implanted with three models of closely spaced CI arrays. The localization results are compared with the ground truth localization results manually generated by an expert.
A validation study was conducted on 129 clinical CTs of CI recipients implanted with three models of closely spaced arrays. Ninety-eight percent of the localization results generated by the proposed method had maximum localization errors lower than one voxel diagonal of the CTs. The mean localization error was 0.13 mm, which was close to the rater's consistency error (0.11 mm). The method also outperformed the existing automatic electrode localization methods in our validation study.
Our validation study shows that our method can localize closely spaced CI arrays with an accuracy close to what is achievable by an expert on clinical CTs. This represents a crucial step toward automating IGCIP and translating it from the laboratory to the clinical workflow.
人工耳蜗植入物（CIs）是为听力严重受损的人提供声音感知的神经假体设备。最近的研究表明，听力结果与电极在耳蜗内的位置之间存在显著相关性。我们已经基于这种相关性开发了一种图像引导的人工耳蜗植入编程（IGCIP）系统，以帮助听力学家对人工耳蜗设备进行编程。我们的 IGCIP 系统中的一个关键步骤是在植入后 CT 中定位 CI 电极。此步骤的现有方法要么不是完全自动化的，要么不够稳健。当 CI 电极紧密间隔时，由于在临床 CT 中它们之间没有强度对比，因此更难以识别单个电极。这项工作的目标是自动分割植入后临床 CT 中的紧密间隔的 CI 电极阵列。
该方法首先通过参考 CT 识别包含耳蜗的边界框。然后，使用强度图像和 VOI 的血管响应来分割可能包含电极阵列的感兴趣区域（ROI）。对于每个 ROI，我们应用体素细化方法生成中轴线上的线。我们详尽地搜索了所有可能的中轴线上的连接。对于每个可能的连接，我们通过在连接的中轴线上选择两个点作为阵列端点，定义 CI 数组中心线候选。对于每个 CI 数组中心线候选，我们使用成本函数来评估其质量，选择成本最低的作为数组中心线。然后，我们使用先验已知的阵列几何模型拟合到中轴线上，以定位单个电极。该方法在植入三种紧密间隔 CI 阵列的 28 例临床 CT 上进行了训练。将定位结果与专家手动生成的地面实况定位结果进行比较。
对植入三种紧密间隔阵列的 129 例 CI 受者的临床 CT 进行了验证研究。该方法生成的 98%的定位结果的最大定位误差低于 CT 的一个体素对角线。平均定位误差为 0.13 毫米，接近评分者的一致性误差（0.11 毫米）。该方法在我们的验证研究中也优于现有的自动电极定位方法。
我们的验证研究表明，该方法可以在临床 CT 上以接近专家的精度定位紧密间隔的 CI 阵列。这代表着向自动 IGCIP 迈进了一步，并将其从实验室转化为临床工作流程。
