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Toward an automatic preoperative pipeline for image-guided temporal bone surgery

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

Abstract

Purpose

Minimally invasive surgery is often built upon a time-consuming preoperative step consisting of segmentation and trajectory planning. At the temporal bone, a complete automation of these two tasks might lead to faster interventions and more reproducible results, benefiting clinical workflow and patient health.

Methods

We propose an automatic segmentation and trajectory planning pipeline for image-guided interventions at the temporal bone. For segmentation, we use a shape regularized deep learning approach that is capable of automatically detecting even the cluttered tiny structures specific for this anatomy. We then perform trajectory planning for both linear and nonlinear interventions on these automatically segmented risk structures.

Results

We evaluate the usability of segmentation algorithms for planning access canals to the cochlea and the internal auditory canal on 24 CT data sets of real patients. Our new approach achieves similar results to the existing semiautomatic method in terms of Dice but provides more accurate organ shapes for the subsequent trajectory planning step. The source code of the algorithms is publicly available.

Conclusion

Automatic segmentation and trajectory planning for various clinical procedures at the temporal bone are feasible. The proposed automatic pipeline leads to an efficient and unbiased workflow for preoperative planning.

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Notes

  1. https://github.com/MECLabTUDA/MUKNO.git, www.gris.tu-darmstadt.de/short/IPCAI2019weights.

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Authors and Affiliations

  1. Department of Computer Science, Technische Universität Darmstadt, Darmstadt, Germany

    Johannes Fauser, Markus Bauer, Wei-Hung Hsu & Anirban Mukhopadhyay

  2. Department of Oto-Rhino-Laryngology, Düsseldorf University Hospital, Düsseldorf, Germany

    Igor Stenin, Julia Kristin, Thomas Klenzner & Jörg Schipper

Authors
  1. Johannes Fauser
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  2. Igor Stenin
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  3. Markus Bauer
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  4. Wei-Hung Hsu
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  5. Julia Kristin
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  6. Thomas Klenzner
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  7. Jörg Schipper
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  8. Anirban Mukhopadhyay
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Correspondence to Johannes Fauser.

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This research was partially funded by the German Research Foundation. The authors declare that they have no conflict of interest.

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Fauser, J., Stenin, I., Bauer, M. et al. Toward an automatic preoperative pipeline for image-guided temporal bone surgery. Int J CARS 14, 967–976 (2019). https://doi.org/10.1007/s11548-019-01937-x

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  • DOI: https://doi.org/10.1007/s11548-019-01937-x

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