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Automated atlas-based segmentation for skull base surgical planning

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Computational surgical planning tools could help develop novel skull base surgical approaches that improve safety and patient outcomes. This defines a need for automated skull base segmentation to improve the usability of surgical planning software. The objective of this work was to design and validate an algorithm for atlas-based automated segmentation of skull base structures in individual image sets for skull base surgical planning.

Methods

Advanced Normalization Tools software was used to construct a synthetic CT template from 6 subjects, and skull base structures were manually segmented to create a reference atlas. Landmark registration followed by Elastix deformable registration was applied to the template to register it to each of the 30 trusted reference image sets. Dice coefficient, average Hausdorff distance, and clinical usability scoring were used to compare the atlas segmentations to those of the trusted reference image sets.

Results

The mean for average Hausdorff distance for all structures was less than 2 mm (mean for 95th percentile Hausdorff distance was less than 5 mm). For structures greater than 2.5 mL in volume, the average Dice coefficient was 0.73 (range 0.59–0.82), and for structures less than 2.5 mL in volume the Dice coefficient was less than 0.7. The usability scoring survey was completed by three experts, and all structures met the criteria for acceptable effort except for the foramen spinosum, rotundum, and carotid artery, which required more than minor corrections.

Conclusion

Currently available open-source algorithms, such as the Elastix deformable algorithm, can be used for automated atlas-based segmentation of skull base structures with acceptable clinical accuracy and minimal corrections with the use of the proposed atlas. The first publicly available CT template and anterior skull base segmentation atlas being released (available at this link: http://hdl.handle.net/1773/46259) with this paper will allow for general use of automated atlas-based segmentation of the skull base.

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Availability of data and material

Synthetic CT template and segmentation file are available for download http://hdl.handle.net/1773/46259.

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Acknowledgements

NK was supported by T32 DC000018-34 from the National Institute on Deafness and Other Communication Disorders awarded to the University of Washington Department of Otolaryngology (P.I., Edward Weaver). RB was supported by Clinical Research Scholars Program, Center for Clinical and Translational Research, Seattle Children’s Hospital. The authors are grateful to Dr. Ian Humphreys for his help with the clinical validation scoring and Kathryn Whitlock, MS for the statistical power analysis.

Funding

This study was funded by the T32 grant DC000018-34 from the National Institute on Deafness and Other Communication Disorders and the Clinical Research Scholars Program grant from Center for Clinical and Translational Research at Seattle Children’s Hospital.

Author information

Authors and Affiliations

  1. Department of Otolaryngology, Head and Neck Surgery, University of Washington, Seattle, WA, USA

    Neeraja Konuthula, Waleed M. Abuzeid, Kris Moe & Randall A. Bly

  2. Department of Radiology, University of Washington, Seattle, WA, USA

    Francisco A. Perez

  3. Division of Radiology, Seattle Children’s Hospital, Seattle, WA, USA

    Francisco A. Perez

  4. Department of Craniofacial Medicine, University of Washington, Seattle, WA, USA

    A. Murat Maga

  5. Craniofacial Center, Seattle Children’s Hospital, Seattle, WA, USA

    A. Murat Maga

  6. Otolaryngology-Head and Neck Surgery, Harborview Medical Center, Seattle, WA, USA

    Kris Moe

  7. Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA

    Blake Hannaford

  8. Division of Pediatric Otolaryngology, Head and Neck Surgery, Seattle Children’s Hospital, Seattle, WA, USA

    Randall A. Bly

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  1. Neeraja Konuthula
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  2. Francisco A. Perez
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  3. A. Murat Maga
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  4. Waleed M. Abuzeid
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Contributions

NK conceived and designed the analysis, contributed data or analysis tools, performed data analysis, and wrote the paper; FP contributed data or analysis tools and edited the paper; AMM contributed data or analysis tools and edited the paper; WMA assisted in validation and edited the paper; KM assisted in validation and edited the paper; BH conceived and designed the analysis, contributed data or analysis tools, and edited the paper; RB conceived and designed the analysis, contributed data or analysis tools, assisted in validation, and edited the paper.

Corresponding author

Correspondence to Randall A. Bly.

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Conflict of Interest

NK declares that they have no conflict of interest. FAP declares that they have no conflict of interest. AMM declares that they have no conflict of interest. WMA declares that they have no conflict of interest. KM is a cofounder of SpiWay, LLC. BH declares that they have no conflict of interest. RB is a cofounder of EigenHealth, Inc., a consultant to SpiWay, LLC, and holds a financial interest of ownership equity with Edus Health, Inc.

Ethics 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.

The study was approved by Seattle Children’s Hospital Institutional Review Board (SCH IRB # STUDY00001830).

Informed Consent

Retrospective study: For this type of study formal consent is not required.

Code availability

Data available for download http://hdl.handle.net/1773/46259.

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Konuthula, N., Perez, F.A., Maga, A.M. et al. Automated atlas-based segmentation for skull base surgical planning. Int J CARS 16, 933–941 (2021). https://doi.org/10.1007/s11548-021-02390-5

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