Noble Jack H, Dawant Benoit M, Warren Frank M, Labadie Robert F
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN 37235, USA.
Otol Neurotol. 2009 Jun;30(4):436-42. doi: 10.1097/MAO.0b013e31819e61ed.
Using automated methods, vital anatomy of the middle ear can be identified in computed tomographic (CT) scans and used to create 3-dimensional (3D) renderings.
Although difficult to master, clinicians compile 2D data from CT scans to envision 3D anatomy. Computer programs exist that can render 3D surfaces but are limited in that ear structures, for example, the facial nerve, can only be visualized after time-intensive manual identification for each scan. Here, we present results from novel computer algorithms that automatically identify temporal bone anatomy (external auditory canal, ossicles, labyrinth, facial nerve, and chorda tympani).
An atlas of the labyrinth, ossicles, and auditory canal was created by manually identifying the structures in a "normal" temporal bone CT scan. Using well-accepted techniques, these structures were automatically identified in (n = 14) unknown CT images by deforming the atlas to match the unknown volumes. Another automatic localization algorithm was implemented to identify the position of the facial nerve and chorda tympani. Results were compared with manual identification by measuring false-positive and false-negative error.
The labyrinth, ossicles, and auditory canal were identified with mean errors less than 0.5 mm. The mean errors in facial nerve and chorda tympani identification were less than 0.3 mm.
Automated identification of temporal bone anatomy is achievable. The presented combination of techniques was successful in accurately identifying temporal bone anatomy. These results were obtained in less than 10 minutes per patient scan using standard computing equipment.
使用自动化方法,可在计算机断层扫描(CT)中识别中耳的重要解剖结构,并用于创建三维(3D)渲染图。
尽管难以掌握,但临床医生会从CT扫描中汇编二维数据以设想三维解剖结构。现有的计算机程序可以渲染三维表面,但存在局限性,例如耳部结构(如面神经),只有在对每次扫描进行耗时的手动识别后才能可视化。在此,我们展示了新型计算机算法的结果,该算法可自动识别颞骨解剖结构(外耳道、听小骨、迷路、面神经和鼓索)。
通过手动识别“正常”颞骨CT扫描中的结构,创建了一个包含迷路、听小骨和耳道的图谱。使用公认的技术,通过使图谱变形以匹配未知体积,在(n = 14)张未知CT图像中自动识别这些结构。实施了另一种自动定位算法来识别面神经和鼓索的位置。通过测量假阳性和假阴性误差,将结果与手动识别进行比较。
迷路、听小骨和耳道的识别平均误差小于0.5毫米。面神经和鼓索识别的平均误差小于0.3毫米。
颞骨解剖结构的自动识别是可以实现的。所展示的技术组合成功地准确识别了颞骨解剖结构。使用标准计算设备,每位患者扫描的结果在不到10分钟内即可获得。