Automated segmentation of the incus and malleus ossicles in conventional tri-dimensional computed tomography images

Abstract

This article proposes a fully automated computational solution to segment the incus and malleus ear ossicles in conventional tri-dimensional X-ray computed tomography images. The solution uses a registration-based segmentation paradigm, followed by image segmentation refinement. It was tested against a dataset comprising 21 computed tomography 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 mm³. The images used were randomly selected from subjects who had had a computed tomography examination 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 the neighbouring voxels in the dataset tested. The results confirm that the automated segmentation of the incus and malleus ossicles in tri-dimensional images acquired from patients with ear-related pathologies, using conventional computed tomography scanners and standard protocols, is feasible, robust and accurate. Thus, the solution developed can be employed efficiently in computed tomography ear examinations to help radiologists and otolaryngologists in the evaluation of bi-dimensional slices by providing the related tri-dimensional model.

Keywords

Medical imaging imaging analysis image registration atlas-based segmentation ear segmentation

Get full access to this article

View all access options for this article.

References

Gentil

Parente

Martins

. The influence of muscles activation on the dynamical behaviour of the tympano-ossicular system of the middle ear. Comput Methods Biomech Biomed Engin 2013; 16(4): 392–402.

Gentil

Parente

Martins

. The influence of the mechanical behaviour of the middle ear ligaments: a finite element analysis. Proc IMechE, Part H: J Engineering in Medicine 2011; 225(1): 68–76.

Gan

Reeves

Wang

. Modeling of sound transmission from ear canal to cochlea. Ann Biomed Eng 2007; 35(12): 2180–2195.

Majdani

Rau

Baron

. A robot-guided minimally invasive approach for cochlear implant surgery: preliminary results of a temporal bone study. Int J Comput Assist Radiol Surg 2009; 4: 475–486.

Gerber

Bell

Gavaghan

. Surgical planning tool for robotically assisted hearing aid implantation. Int J Comput Assist Radiol Surg 2014; 9(1): 11–20.

Crescenzio

Fantini

Ciocca

. Design and manufacturing of ear prosthesis by means of rapid prototyping technology. Proc IMechE, Part H: J Engineering in Medicine 2011; 225(3): 296–302.

Counter

. Implantable hearing aids. Proc IMechE, Part H: J Engineering in Medicine 2008; 222(6): 837–852.

Ferreira

Gentil

Tavares

JMRS

. Segmentation algorithms for ear image data towards biomechanical studies. Comput Methods Biomech Biomed Engin 2014; 17(8): 888–904.

Oliveira

FPM

Tavares

JMRS

. Medical image registration: a review. Comput Methods Biomech Biomed Engin 2014; 17(2): 73–93.

10.

Bradshaw

Curthoys

Todd

. A mathematical model of human semicircular canal geometry: a new basis for interpreting vestibular physiology. J Assoc Res Otolaryngol 2010; 11: 145–159.

11.

Lee

Chan

Salisbury

. Reconstruction and exploration of virtual middle-ear models derived from micro-CT datasets. Hear Res 2010; 263: 198–203.

12.

Christensen

Dill

. Automatic measurement of the labyrinth using image registration and a deformable inner ear atlas. Acad Radiol 2003; 10(9): 988–999.

13.

Noble

Labadie

Majdani

. Automatic segmentation of intracochlear anatomy in conventional CT. IEEE Trans Biomed Eng 2011; 58(9): 2625–2632.

14.

Noble

Warren

Labadie

. Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature values. Med Phys 2008; 35(12): 5375–5384.

15.

Noble

Dawant

Warren

. Automatic identification and 3D rendering of temporal bone anatomy. Otol Neurotol 2009; 30: 436–442.

16.

Feeman

. The mathematics of medical imaging: a beginner’s guide. New York: Springer, 2010.

17.

Orchard

. Efficient least squares multimodal registration with a globally exhaustive alignment search. IEEE Trans Image Process 2007; 16(10): 2526–2534.

18.

Rueckert

Sonoda

Hayes

. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 1999; 18(8): 712–721.

19.

Press

Teukolsky

Vetterling

. Numerical recipes: the art of scientific computing. 3rd ed. New York: Cambridge University Press, 2007.

20.

Collignon

Maes

Vandermeulen

. Multimodality medical image registration by maximization of mutual information. IEEE Trans Med Imaging 1997; 16(2): 187–198.

21.

Grabner

Janke

Budge

. Symmetric atlasing and model based segmentation: an application to the hippocampus in older adults. In: 9th international conference on medical image computing and computer assisted intervention – MICCAI 2006 (ed Larsen

Nielsen

Sporring

), Copenhagen, 1–6 October 2006. Copenhagen: Springer Verlag.

22.

Yushkevich

Piven

Hazlett

. User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006; 31(3): 1116–1128.

23.

Lorensen

Cline

. Marching cubes: a high resolution 3D surface construction algorithm. Comput Graph 1987; 21(4): 163–169.

24.

Hsieh

M-S

Lee

F-P

Tsai

M-D

. A virtual reality ear ossicle surgery simulator using three-dimensional computer tomography. J Med Biol Eng 2010; 30(1): 57–63.