Facial Nerves

The process used to automatically identify the labyrinth, ossicles, and external auditory canal relies on atlas-based registration, a common technique in the field of medical imaging. The principle of atlas-based registration is that an image of a known subject can be transformed automatically such that the anatomical structures of the known subject are made to overlap with the corresponding structures in the image of an unknown subject. Given a perfect registration, transformed labels from the known atlas exactly identify the location of the structures in the unknown image. Figure 1 shows an example of atlas-based registration as is typically used in neurosurgical applications. The underlying assumption of this method is that the images of different subjects are topologically similar such that a one-to-one mapping between all corresponding anatomical structures can be established via a smooth transformation. For patients with normal anatomy, this assumption is valid in the anatomical regions surrounding the labyrinth, ossicles, and external auditory canal. Using atlas-based registration methods described previously [6 ,7 ,8 ] and an atlas constructed with a CT of a “normal” subject, we created a registration approach to allow labeling of these structures on temporal bone CT’s.
For the anatomical region surrounding the facial nerve and chorda tympani, topological similarity between images cannot be assumed due to the highly variable pneumatized bone. Therefore, the facial nerve and chorda tympani are identified using another approach, the navigated optimal medial axis and deformable-model algorithm (NOMAD) [8 ]. NOMAD is a general framework for localizing tubular structures. Statistical a-priori intensity and shape information about the structure is stored in a model. Atlas-based registration is used to roughly align this model information to an unknown CT. Using the model information, the optimal axis of the structure is identified. The full structure is then identified by expanding this centerline using deformable-model (ballooning) techniques.
To validate our process, we quantified automated identification error as follows: (1) The temporal bone structures were manually identified in all CT scans by a student rater then verified and corrected by an experienced surgeon. (2) Binary volumes were generated from the manual delineations, with a value of 1 indicating an internal voxel and 0 being an external voxel. (3) Surface voxels were identified in both the automatic and manually generated volumes. (4) For each voxel on the automatic surface, the distance to the closest manual surface voxel was computed. We call this the false positive error distance (FP). Similarly, for each voxel on the manual surface, the distance to the closest automatic surface voxel was computed, which we call the false negative error distance (FN) (See Figure 2). We compute both FP and FN errors because, as shown in Figure 2, the FP and FN errors are not necessarily the same for a given point. In fact, to properly characterize identification errors, computing both distances is necessary.