Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.
Int J Comput Assist Radiol Surg. 2017 Nov;12(11):1937-1944. doi: 10.1007/s11548-017-1658-6. Epub 2017 Aug 29.
To develop a time-efficient automated segmentation approach that could identify critical structures in the temporal bone for visual enhancement and use in surgical simulation software.
An atlas-based segmentation approach was developed to segment the cochlea, ossicles, semicircular canals (SCCs), and facial nerve in normal temporal bone CT images. This approach was tested in images of 26 cadaver bones (13 left, 13 right). The results of the automated segmentation were compared to manual segmentation visually and using DICE metric, average Hausdorff distance, and volume similarity.
The DICE metrics were greater than 0.8 for the cochlea, malleus, incus, and the SCCs combined. It was slightly lower for the facial nerve. The average Hausdorff distance was less than one voxel for all structures, and the volume similarity was 0.86 or greater for all structures except the stapes.
The atlas-based approach with rigid body registration of the otic capsule was successful in segmenting critical structures of temporal bone anatomy for use in surgical simulation software.
开发一种高效的自动化分割方法，以便识别颞骨中的关键结构，用于视觉增强和手术模拟软件。
采用基于图谱的分割方法对正常颞骨 CT 图像中的耳蜗、听小骨、半规管（SCC）和面神经进行分割。该方法在 26 具尸体骨骼（13 具左侧，13 具右侧）的图像上进行了测试。自动分割的结果通过视觉和 DICE 度量、平均 Hausdorff 距离和体积相似性与手动分割进行了比较。
耳蜗、锤骨、砧骨和 SCC 组合的 DICE 度量值大于 0.8。面神经的稍低。所有结构的平均 Hausdorff 距离均小于一个体素，除镫骨外，所有结构的体积相似性均为 0.86 或更高。
基于图谱的方法结合耳囊的刚体配准，成功地对颞骨解剖的关键结构进行了分割，可用于手术模拟软件。
