Automated segmentation of the incus and malleus ossicles in conventional 3D CT images

This paper proposes a fully automated computational solution to segment the incus and malleus ear ossicles in conventional tri-dimensional (3D) X-ray computed tomography (CT) images. The solution uses a registration-based segmentation paradigm, followed by image segmentation refinement. It was tested against a dataset comprising 21 CT volumetric images of the ear acquired using standard protocols and with resolutions varying from 0.162×0.162×0.6 to 0.166×0.166×1.0 mm. The images used were randomly selected from subjects that had had a CT exam of the ear due to ear related pathologies. Dice's coefficient and the Hausdorff distance were used to compare the results of the automated segmentation against those of a manual segmentation performed by two experts. The mean agreement between automated and manual segmentations was equal to 0.956 (Dice's coefficient), and the mean Hausdorff distance among the shapes obtained was 1.14 mm, which is approximately equal to the maximum distance between neighbouring voxels in the dataset tested. The results confirm that the automated segmentation of the incus and malleus ossicles in 3D images acquired from patients with ear related pathologies, using conventional CT scanners and standard protocols, is feasible, robust and accurate. Thus, the solution developed can be employed efficiently in CT ear exams to help radiologists and otolaryngologists in the evaluation of bi-dimensional (2D) slices by providing the related 3D model.