Zhang Dongqing, Liu Yuan, Noble Jack H, Dawant Benoit M
Vanderbilt University, Department of Electrical Engineering and Computer Science, Nashville, Tennessee, United States.
J Med Imaging (Bellingham). 2017 Oct;4(4):044007. doi: 10.1117/1.JMI.4.4.044007. Epub 2017 Dec 8.
Cochlear implants (CIs) use electrode arrays that are surgically inserted into the cochlea to stimulate frequency-mapped nerve endings to treat patients with hearing loss. CIs are programmed postoperatively by audiologists using behavioral tests without information on electrode-cochlea spatial relationship. We have recently developed techniques to segment the intracochlear anatomy and to localize individual contacts in clinically acquired computed tomography (CT) images. Using this information, we have proposed a programming strategy that we call image-guided CI programming (IGCIP), and we have shown that it significantly improves outcomes for both adult and pediatric recipients. One obstacle to large-scale deployment of this technique is the need for manual intervention in some processing steps. One of these is the rough registration of images prior to the use of automated intensity-based algorithms. Although seemingly simple, the heterogeneity of our image set makes this task challenging. We propose a solution that relies on the automated random forest-based localization of multiple landmarks used to estimate an initial transformation with a point-based registration method. Results show that it produces results that are equivalent to a manual initialization. This work is an important step toward the full automation of IGCIP.
人工耳蜗（CI）使用通过手术插入耳蜗的电极阵列来刺激按频率映射的神经末梢，以治疗听力损失患者。术后，听力学家使用行为测试对人工耳蜗进行编程，而无需有关电极与耳蜗空间关系的信息。我们最近开发了一些技术，可对耳蜗内的解剖结构进行分割，并在临床获取的计算机断层扫描（CT）图像中定位各个电极触点。利用这些信息，我们提出了一种编程策略，称为图像引导的人工耳蜗编程（IGCIP），并且我们已经表明，它能显著改善成人和儿童接受者的治疗效果。大规模应用这项技术的一个障碍是在某些处理步骤中需要人工干预。其中之一是在使用基于强度的自动算法之前对图像进行粗略配准。尽管看似简单，但我们图像集的异质性使得这项任务具有挑战性。我们提出了一种解决方案，该方案依赖于基于自动随机森林的多个地标定位，用于通过基于点的配准方法估计初始变换。结果表明，它产生的结果与手动初始化相当。这项工作是迈向IGCIP完全自动化的重要一步。
