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Stochastic Anomaly Simulation for Maxilla Completion from Cone-Beam Computed Tomography

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2024 (MICCAI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15008))

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Abstract

Automated alveolar cleft defect restoration from cone beam computed tomography (CBCT) remains a challenging task, considering large morphological variations due to inter-subject abnormal maxilla development processes and a small cohort of clinical data. Existing works relied on rigid or deformable registration to borrow bony tissues from an unaffected side or a template for bony tissue filling. However, they lack harmony with the surrounding irregular maxilla structures and are limited when faced with bilateral defects. In this paper, we present a stochastic anomaly simulation algorithm for defected CBCT generation, combating limited clinical data and burdensome volumetric image annotation. By respecting the facial fusion process, the proposed anomaly simulation algorithm enables plausible data generation and relieves gaps from clinical data. We propose a weakly supervised volumetric inpainting framework for cleft defect restoration and maxilla completion, taking advantage of anomaly simulation-based data generation and the recent success of deep image inpainting techniques. Extensive experimental results demonstrate that our approach effectively restores defected CBCTs with performance gains over state-of-the-art methods.

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Acknowledgments

This work was supported in part by National Natural Science Foundation of China under Grant 62272011, 61876008, and 82071172, Beijing Natural Science Foundation 7232337.

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Authors and Affiliations

  1. School of Intelligence Science and Technology, Key Laboratory of Machine Perception (MOE), State Key Laboratory of General Artificial Intelligence, Peking University, Beijing, 100871, China

    Yixiao Guo, Yuru Pei & Hongbin Zha

  2. School of Stomatology, Peking University, Beijing, China

    Si Chen, Zhi-bo Zhou & Tianmin Xu

Authors
  1. Yixiao Guo
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  2. Yuru Pei
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  3. Si Chen
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  4. Zhi-bo Zhou
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  5. Tianmin Xu
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  6. Hongbin Zha
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Corresponding author

Correspondence to Yuru Pei .

Editor information

Editors and Affiliations

  1. Children’s National Hospital/George Washington University, Washington, DC, USA

    Marius George Linguraru

  2. The Chinese University of Hong Kong, Hong Kong, China

    Qi Dou

  3. Technical University of Denmark, Kgs Lyngby, Denmark

    Aasa Feragen

  4. Imperial College London, London, UK

    Stamatia Giannarou

  5. Imperial College London, London, UK

    Ben Glocker

  6. Universitat de Barcelona, Barcelona, Spain

    Karim Lekadir

  7. Helmholtz Munich, Technical University of Munich and King’s College London, Munich, Germany

    Julia A. Schnabel

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The authors have no competing interests to declare that are relevant to the content of this article.

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Guo, Y., Pei, Y., Chen, S., Zhou, Zb., Xu, T., Zha, H. (2024). Stochastic Anomaly Simulation for Maxilla Completion from Cone-Beam Computed Tomography. In: Linguraru, M.G., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024. MICCAI 2024. Lecture Notes in Computer Science, vol 15008. Springer, Cham. https://doi.org/10.1007/978-3-031-72111-3_63

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  • DOI: https://doi.org/10.1007/978-3-031-72111-3_63

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-72110-6

  • Online ISBN: 978-3-031-72111-3

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