Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
Department of Otolaryngology - Head and Neck Surgery, The Ohio State University and Nationwide Children's Hospital, Columbus, OH, USA.
Int J Comput Assist Radiol Surg. 2021 Mar;16(3):363-373. doi: 10.1007/s11548-020-02304-x. Epub 2021 Feb 13.
To develop an automated segmentation approach for cochlear microstructures [scala tympani (ST), scala vestibuli (SV), modiolus (Mod), mid-modiolus (Mid-Mod), and round window membrane (RW)] in clinical cone beam computed tomography (CBCT) images of the temporal bone for use in surgical simulation software and for preoperative surgical evaluation.
This approach was developed using the publicly available OpenEar (OE) Library that includes temporal bone specimens with spatially registered CBCT and 3D micro-slicing images. Five of these datasets were spatially aligned to our internal OSU atlas. An atlas of cochlear microstructures was created from one of the OE datasets. An affine registration of this atlas to the remaining OE CBCT images was used for automatically segmenting the cochlear microstructures. Quantitative metrics and visual review were used for validating the automatic segmentations.
The average DICE metrics were 0.77 and 0.74 for the ST and SV, respectively. The average Hausdorff distance (AVG HD) was 0.11 mm and 0.12 mm for both scalae. The mean distance between the centroids for the round window was 0.32 mm, and the mean AVG HD was 0.09 mm. The mean distance and angular rotation between the mid-modiolar axes were 0.11 mm and 9.8 degrees, respectively. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer.
An atlas-based approach using 3D micro-slicing data and affine spatial registration in the cochlear region was successful in segmenting cochlear microstructures of temporal bone anatomy for use in simulation software and potentially for pre-surgical planning and rehearsal.
开发一种自动分割耳蜗微结构[鼓阶（ST）、前庭阶（SV）、耳蜗轴（Mod）、中耳蜗轴（Mid-Mod）和圆窗膜（RW）]的方法，以便在临床锥形束 CT（CBCT）颞骨图像中用于手术模拟软件，并进行术前手术评估。
该方法使用公开的 OpenEar（OE）库开发，该库包括具有空间配准的 CBCT 和 3D 微切片图像的颞骨标本。其中 5 个数据集在空间上与我们内部的 OSU 图谱对齐。从一个 OE 数据集创建了耳蜗微结构图谱。该图谱与其余 OE CBCT 图像的仿射配准用于自动分割耳蜗微结构。使用定量指标和视觉检查来验证自动分割。
ST 和 SV 的平均 DICE 度量值分别为 0.77 和 0.74。两个音阶的平均 Hausdorff 距离（AVG HD）分别为 0.11mm 和 0.12mm。圆窗的质心之间的平均距离为 0.32mm，平均 AVG HD 为 0.09mm。中耳蜗轴之间的平均距离和角旋转分别为 0.11mm 和 9.8 度。从视觉上看，分割的结构准确且与专家观察者手动追踪的结构相似。
在耳蜗区域使用 3D 微切片数据和仿射空间配准的图谱方法成功地分割了颞骨解剖结构的耳蜗微结构，可用于模拟软件，并且可能用于术前计划和排练。
