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. 2020 Apr;61:101659. doi: 10.1016/j.media.2020.101659. Epub 2020 Jan 28.
Cochlear implants (CIs) are used to treat subjects with hearing loss. In a CI surgery, an electrode array is inserted into the cochlea to stimulate auditory nerves. After surgery, CIs need to be programmed. Studies have shown that the cochlea-electrode spatial relationship derived from medical images can guide CI programming and lead to significant improvement in hearing outcomes. We have developed a series of algorithms to segment the inner ear anatomy and localize the electrodes. But, because clinical head CT images are acquired with different protocols, the field of view and orientation of the image volumes vary greatly. As a consequence, visual inspection and manual image registration to an atlas image are needed to document their content and to initialize intensity-based registration algorithms used in our processing pipeline. For large-scale evaluation and deployment of our methods these steps need to be automated. In this article we propose to achieve this with a deep convolutional neural network (CNN) that can be trained end-to-end to classify a head CT image in terms of its content and to localize landmarks. The detected landmarks can then be used to estimate a point-based registration with the atlas image in which the same landmark set's positions are known. We achieve 99.5% classification accuracy and an average localization error of 3.45 mm for 7 landmarks located around each inner ear. This is better than what was achieved with earlier methods we have proposed for the same tasks.
人工耳蜗是用于治疗听力损失患者的一种设备。在人工耳蜗手术中，将电极阵列插入耳蜗以刺激听神经。手术后，需要对人工耳蜗进行编程。研究表明，从医学图像中得出的耳蜗-电极空间关系可以指导人工耳蜗编程，并显著改善听力效果。我们已经开发了一系列算法来分割内耳解剖结构并定位电极。但是，由于临床头部 CT 图像是使用不同的协议获取的，因此图像体积的视野和方向有很大差异。因此，需要对其内容进行目视检查和手动图像配准到图谱图像，并初始化我们处理管道中使用的基于强度的配准算法。对于我们方法的大规模评估和部署，这些步骤需要自动化。在本文中，我们提出使用深度卷积神经网络（CNN）来实现这一点，该网络可以端到端地进行训练，以便根据内容对头部 CT 图像进行分类，并定位地标。然后可以使用检测到的地标来估计与图谱图像的基于点的配准，在图谱图像中已知相同地标集的位置。我们实现了 99.5%的分类准确率和 7 个位于每个内耳周围的地标平均定位误差为 3.45mm。这优于我们为相同任务提出的早期方法所取得的结果。
