Automatic identification and 3D rendering of temporal bone anatomy

Abstract

Hypothesis: 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.

Background: 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).

Methods: 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.

Results: 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.

Conclusion: 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.

Figure 1. Example of inter-patient image registration. A registration atlas and target temporal bone images are shown, as well as the target image registered to the atlas

Example of atlas based registration. Shown are delineations of the ventricles in the (left-to-right) sagittal, axial, and coronal views of the MR in the bottom row. These delineations are overlaid on (top row) the MR of another subject and (middle row) the same subject from the top row after registration to the image in the bottom row.

Figure 2. Methods for calculating error between automatically and manually generated structure surfaces. The types of error are visually illustrated in a 2D example

Methods for calculating error between automatically and manually generated structure surfaces. The AM error along the automatic structure surface is calculated as the closest distance to the manual surface. Similarly, MA error is calculated for the manual surface as the closest distance to the automatically generated surface. Zero error is seen where the contours overlap.

Figure 3. This figure illustrates disagreement between manual identification of the facial nerve and chorda and the localization results using atlas-based methods

Facial nerve and chorda segmentation results using atlas-based methods (FN-ab, CH-ab) for Volume 1, right ear. The true solutions for the facial nerve and chorda are delineated in purple and green. A significant error can be seen between the automatic and true solutions.

Figure 4. 3D visualizations and CT cross section of automatic localization results

3D renderings and CT cross section automatic segmentation results for three volumes. All segmentation results appear qualitatively accurate.

Figure 5

Localized structure contours in the CT planes and 3D visualizations presented to the end-user.