IEEE Trans Biomed Eng. 2018 Jan;65(1):178-188. doi: 10.1109/TBME.2017.2697916. Epub 2017 Apr 25.
Facial nerve segmentation is of considerable importance for preoperative planning of cochlear implantation. However, it is strongly influenced by the relatively low resolution of the cone-beam computed tomography (CBCT) images used in clinical practice. In this paper, we propose a super-resolution classification method, which refines a given initial segmentation of the facial nerve to a subvoxel classification level from CBCT/CT images. The super-resolution classification method learns the mapping from low-resolution CBCT/CT images to high-resolution facial nerve label images, obtained from manual segmentation on micro-CT images. We present preliminary results on dataset, 15 ex vivo samples scanned including pairs of CBCT/CT scans and high-resolution micro-CT scans, with a leave-one-out evaluation, and manual segmentations on micro-CT images as ground truth. Our experiments achieved a segmentation accuracy with a Dice coefficient of , surface-to-surface distance of , and Hausdorff distance of . We compared the proposed technique to two other semi-automated segmentation software tools, ITK-SNAP and GeoS, and show the ability of the proposed approach to yield subvoxel levels of accuracy in delineating the facial nerve.
面神经分割对面神经植入术的术前规划具有重要意义。然而，它受到临床实践中使用的锥形束 CT（CBCT）图像分辨率相对较低的强烈影响。在本文中，我们提出了一种超分辨率分类方法，该方法将给定的面神经初始分割从 CBCT/CT 图像细化到亚像素分类水平。该超分辨率分类方法学习从低分辨率 CBCT/CT 图像到从微 CT 图像手动分割获得的高分辨率面神经标记图像的映射。我们在数据集上呈现了初步结果，包括 15 个离体样本扫描，包括 CBCT/CT 扫描对和高分辨率微 CT 扫描，采用留一法评估和微 CT 图像的手动分割作为地面实况。我们的实验实现了分割精度为 Dice 系数为 0.936，表面到表面距离为 0.546mm，Hausdorff 距离为 2.062mm。我们将提出的技术与另外两种半自动分割软件工具 ITK-SNAP 和 GeoS 进行了比较，并展示了该方法在描绘面神经方面能够达到亚像素级精度的能力。
