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Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks

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Part of the book series:Lecture Notes in Computer Science ((LNIP,volume 12439))

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Abstract

We present an automated method for extrapolating missing regions in label data of the skull in an anatomically plausible manner. The ultimate goal is to design patient-specific cranial implants for correcting large, arbitrarily shaped defects of the skull that can, for example, result from trauma of the head.

Our approach utilizes a 3D statistical shape model (SSM) of the skull and a 2D generative adversarial network (GAN) that is trained in an unsupervised fashion from samples of healthy patients alone. By fitting the SSM to given input labels containing the skull defect, a first approximation of the healthy state of the patient is obtained. The GAN is then applied to further correct and smooth the output of the SSM in an anatomically plausible manner. Finally, the defect region is extracted using morphological operations and subtraction between the extrapolated healthy state of the patient and the defective input labels.

The method is trained and evaluated based on data from the MICCAI 2020 AutoImplant challenge. It produces state-of-the art results on regularly shaped cut-outs that were present in the training and testing data of the challenge. Furthermore, due to unsupervised nature of the approach, the method generalizes well to previously unseen defects of varying shapes that were only present in the hidden test dataset.

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Author information

Authors and Affiliations

  1. 1000shapes GmbH, Berlin, Germany

    Pedro Pimentel, Angelika Szengel, Moritz Ehlke, Hans Lamecker, Stefan Zachow & Heiko Ramm

  2. Zuse Institute Berlin (ZIB), Berlin, Germany

    Stefan Zachow

  3. San Pablo Catholic University, Arequipa, Peru

    Laura Estacio

  4. Department of Neurosurgery, University Medical Center Regensburg, Regensburg, Germany

    Christian Doenitz

Authors
  1. Pedro Pimentel

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  2. Angelika Szengel

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  3. Moritz Ehlke

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  4. Hans Lamecker

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  5. Stefan Zachow

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  6. Laura Estacio

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  7. Christian Doenitz

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  8. Heiko Ramm

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Corresponding author

Correspondence toPedro Pimentel.

Editor information

Editors and Affiliations

  1. Graz University of Technology, Graz, Austria

    Jianning Li

  2. Graz University of Technology, Graz, Austria

    Jan Egger

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Cite this paper

Pimentel, P.et al. (2020). Automated Virtual Reconstruction of Large Skull Defects using Statistical Shape Models and Generative Adversarial Networks. In: Li, J., Egger, J. (eds) Towards the Automatization of Cranial Implant Design in Cranioplasty. AutoImplant 2020. Lecture Notes in Computer Science(), vol 12439. Springer, Cham. https://doi.org/10.1007/978-3-030-64327-0_3

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Softcover Book
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