Atlas-Based Segmentation of Temporal Bone Anatomy

  • Kimerly A. PowellEmail author
  • Tong Liang
  • Brad Hittle
  • Don Stredney
  • Thomas Kerwin
  • Gregory J. Wiet
Original Article



To develop a time-efficient automated segmentation approach that could identify critical structures in the temporal bone for visual enhancement and use in surgical simulation software.


An atlas-based segmentation approach was developed to segment the cochlea, ossicles, semicircular canals (SCCs), and facial nerve in normal temporal bone CT images. This approach was tested in images of 26 cadaver bones (13 left, 13 right). The results of the automated segmentation were compared to manual segmentation visually and using DICE metric, average Hausdorff distance, and volume similarity.


The DICE metrics were greater than 0.8 for the cochlea, malleus, incus, and the SCCs combined. It was slightly lower for the facial nerve. The average Hausdorff distance was less than one voxel for all structures, and the volume similarity was 0.86 or greater for all structures except the stapes.


The atlas-based approach with rigid body registration of the otic capsule was successful in segmenting critical structures of temporal bone anatomy for use in surgical simulation software.


Atlas-based segmentation Image registration Surgical simulation Temporal bone anatomy 



This research was supported by NIDCD/NIH 1R01-DC011321.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

For this type of study, formal consent is not required.


  1. 1.
    Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW (2010) Elastix: a toolbox for intensity based medical image registration. IEEE Trans Med Imaging 29:196–205CrossRefPubMedGoogle Scholar
  2. 2.
    Shamonin DP, Bron EE, Lelieveldt BPF, Smits M, Klein S, Staring M (2014) Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer’s disease. Front Neuroinform 7:1–15Google Scholar
  3. 3.
    Klein S, Staring M (2014) Elastix the manual.
  4. 4.
    Otsu N (1979) A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern SMC 9:62–66CrossRefGoogle Scholar
  5. 5.
    Gonzalez RC, Woods RE (2008) Digital image processing. Pearson Prentice Hall Inc., Upper Saddle RiverGoogle Scholar
  6. 6.
    Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(29):1–28Google Scholar
  7. 7.
    Zijdenbos AP, Dawant BM, Margolin RA, Palmer A (1994) Morphometrics analysis of white matter lesions in MR images: method and validation. IEEE Trans Med Imaging 13(4):716–724CrossRefPubMedGoogle Scholar
  8. 8.
    Noble JH, Labadie RF, Majdani O, Dawant BM (2010) Automatic segmentation of intra-cochlear anatomy in conventional CT. IEEE Trans Biomed Eng 58(9):2625–2632CrossRefGoogle Scholar
  9. 9.
    Noble JH, Warren FM, Labadie RF, Dawant BM (2008) Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature analysis. Med Phys 35:5375–5384Google Scholar
  10. 10.
    Noble JH, Dawant BM, Warren FM, Labadie RF (2009) Automatic identification and 3D rendering of temporal bone anatomy. Otol Neurotol 30:346–442Google Scholar
  11. 11.
    Rodt T, Raiu P, Becker H, Bartling S, Kacher DF, Anderson M, Jolesz FA, Kikinis R (2002) 3D visualisation of the middle ear and adjacent structures using reconstructed multi-slice CT datasets, correlating 3D images and virtual endoscopy to the 2D cross-sectional images. Neuroradiology 44:783–790CrossRefPubMedGoogle Scholar
  12. 12.
    Seemann MD, Seemann O, Bonel H, Suckfull M, Englmeier K-H, Naumann A, Allen CM, Reiser MF (1999) Evaluation of the middle and inner ear structures: comparison of hybrid rendering, virtual endoscopy and axial 2D source images. Eur Radiol 9:1851–1858CrossRefPubMedGoogle Scholar
  13. 13.
    Chan S, Li P, Locketz G, Salisbury K, Blevins NH (2016) High fidelity haptic and visual rendering for patient-specific simulation of temporal bone surgery. Comput Assist Surg 21:85–101CrossRefGoogle Scholar
  14. 14.
    Hassan K, Dort JC, Sutherland GR, Chan S (2016) Evaluation of software tools for segmentation of temporal bone anatomy. Proc Med Meets Virtual Real 22:130–133Google Scholar
  15. 15.
    Arora A, Swords C, Khemani S, Awad Z, Darzi A, Singh A, Tolley N (2014) Virtual reality case-specific rehearsal in temporal bone surgery: a preliminary evaluation. Int J Surg 12:141–145CrossRefPubMedGoogle Scholar
  16. 16.
    Lee DH, Chan S, Salisbury C, Kim N, Salisbury K, Puria S, Blevins NH (2010) Reconstruction and exploration of virtual middle-ear models derived from micro-CT datasets. Hear Res 263:198–203CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Rafferty MA, Siewerdsen JH, Chan Y, Daly MJ, Moseley DJ, Jaffray DA, Irish JC (2006) Intraoperative cone-beam CT for guidance of temporal bone surgery. Otolaryngol Head Neck Surg 134:801–808CrossRefPubMedGoogle Scholar
  18. 18.
    Barker E, Trimble K, Chan H, Ramsden J, Nithiananthan S, James A, Bachar G, Daly M, Irish J, Siewerdsen J (2009) Intraoperative use of cone-beam computed tomography in a cadaveric ossified cochlea model. Otolaryngol Head Neck Surg 140:697–702CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Erovic BM, Chan HHL, Daly MJ, Pothier DD, Yu E, Coulson C, Lai P, Irish JC (2014) Intraoperative cone-beam computed tomography and multi-slice computed tomography in temporal bone imaging for surgical treatment. Otol Neurotol 150:107–114Google Scholar
  20. 20.
    Uneri A, Schafer S, Mirota DJ, Nithiananthan S, Otake Y, Taylor RH, Siewerdsen JH (2012) TREK: and integrated system architecture for intraoperative cone-beam CT-guided surgery. Int J CARS 7:159–173CrossRefGoogle Scholar

Copyright information

© CARS 2017

Authors and Affiliations

  1. 1.Department of Biomedical InformaticsThe Ohio State UniversityColumbusUSA
  2. 2.Department of Electrical and Computer EngineeringThe Ohio State UniversityColumbusUSA
  3. 3.Ohio Supercomputer CenterColumbusUSA
  4. 4.Department of OtolaryngologyThe Ohio State University and Nationwide Children’s HospitalColumbusUSA
  5. 5.Department of OtolaryngologyThe Ohio State UniversityColumbusUSA

Personalised recommendations