Advertisement

Atlas-based segmentation of temporal bone surface structures

  • Kimerly A. PowellEmail author
  • Tanisha Kashikar
  • Brad Hittle
  • Don Stredney
  • Thomas Kerwin
  • Gregory J. Wiet
Original Article
  • 72 Downloads

Abstract

Purpose

To develop a time-efficient automated segmentation approach that could identify surface structures on the temporal bone for use in surgical simulation software and preoperative surgical training.

Methods

An atlas-based segmentation approach was developed to segment the tegmen, sigmoid sulcus, exterior auditory canal, interior auditory canal, and posterior canal wall in normal temporal bone CT images. This approach was tested in images of 20 cadaver bones (10 left, 10 right). The results of the automated segmentation were compared to manual segmentation using quantitative metrics of similarity, Mahalanobis distance, average Hausdorff distance, and volume similarity.

Results

The Mahalanobis distance was less than 0.232 mm for all structures. The average Hausdorff distance was less than 0.464 mm for all structures except the posterior canal wall and external auditory canal for the right bones. Volume similarity was 0.80 or greater for all structures except the sigmoid sulcus that was 0.75 for both left and right bones. Visually, the segmented structures were accurate and similar to that manually traced by an expert observer.

Conclusions

An atlas-based approach using a deformable registration of a Gaussian-smoothed temporal bone image and refinements using surface landmarks was successful in segmenting surface structures of temporal bone anatomy for use in pre-surgical planning and training.

Keywords

Atlas-based segmentation Image registration Surgical simulation Pre-surgical planning Temporal bone anatomy 

Notes

Acknowledgements

This research was supported by NIDCD/NIH 1R01-DC011321 and by funding from Nationwide Children’s Hospital, Columbus, OH.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the Ohio State University Institutional Review Board and have been performed in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Funding

This study was funded by NIDCD/NIH 1R01-DC011321.

References

  1. 1.
    Powell KA, Liang T, Hittle B, Stredney D, Kerwin T, Wiet GJ (2017) Atlas-based segmentation of temporal bone anatomy. Int J Comput Assist Radiol Surg 12(11):1937–1944CrossRefGoogle Scholar
  2. 2.
    Klein S, Staring M, Murphy K, Viergever MA, Pluim JPW (2010) Elastix: a toolbox for intensity based medical image registration. IEEE Trans Med Imaging 29:196–205CrossRefGoogle Scholar
  3. 3.
    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 Neuroinformatics 7:1–15Google Scholar
  4. 4.
    Klein S, Staring M (2014) Elastix the manual (http://elastix.isi.uu.nl/)
  5. 5.
    Liao PS, Chen TS, Chung PC (2001) A fast algorithm for multilevel thresholding. J Inf Sci Eng 17:713–727Google Scholar
  6. 6.
    Danielsson PE (1980) Euclidean distance mapping. Comput Graph Image Process 14:227–248CrossRefGoogle Scholar
  7. 7.
  8. 8.
    Gonzalez RC, Woods RE (2008) Digital image processing. Pearson Prentice Hall Inc, Englewood CliffsGoogle Scholar
  9. 9.
    Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(29):1–28Google Scholar
  10. 10.
    Kerwin T, Wiet G, Stredney D, Shen HW (2012) Automatic scoring of virtual mastoidectomies using expert examples. Int J Comput Assist Radiol Surg 7(1):1–11CrossRefGoogle Scholar
  11. 11.
    Wiet G, Hittle B, Kerwin T, Stredney D (2012) Translating surgical metrics into automated assessments. Stud Health Technol Inform 173:543–548Google Scholar

Copyright information

© CARS 2019

Authors and Affiliations

  1. 1.Department of Biomedical InformaticsThe Ohio State UniversityColumbusUSA
  2. 2.Ohio University Heritage College of OsteopathicOhio UniversityAthensUSA
  3. 3.Interface LaboratoryThe Ohio State UniversityColumbusUSA
  4. 4.Department of OtolaryngologyNationwide Children’s Hospital and The Ohio State UniversityColumbusUSA

Personalised recommendations