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A Self-Supervised Deep Framework for Reference Bony Shape Estimation in Orthognathic Surgical Planning.

Deqiang Xiao1, Hannah Deng2, Tianshu Kuang2

  • 1Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC 27599, USA.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|December 20, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a self-supervised deep learning framework to automatically create reference facial bony shape models for virtual orthognathic surgery. This approach improves planning accuracy by providing objective guidance for correcting jaw deformities.

Keywords:
Orthognathic surgical planningPoint-cloud networkShape estimation

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Area of Science:

  • Medical imaging
  • Computer-aided surgery
  • 3D modeling

Background:

  • Virtual orthognathic surgery planning relies on 3D facial bony models but lacks objective guidance, leading to experience-dependent, suboptimal results.
  • Accurate reference facial bony shape models representing normal anatomy are crucial for improving surgical planning and patient outcomes.

Purpose of the Study:

  • To propose a self-supervised deep learning framework for automatically estimating patient-specific reference facial bony shape models.
  • To enhance the accuracy and objectivity of virtual orthognathic surgical planning.

Main Methods:

  • Developed an end-to-end trainable deep framework comprising a simulator and a corrector.
  • The simulator generates simulated deformed bones from patient data, and the corrector restores them to a normal-looking state.
  • Trained the network using a self-supervised approach on a clinical dataset.

Main Results:

  • The proposed framework successfully generates clinically acceptable, patient-specific reference facial bony shape models.
  • The method significantly outperforms a state-of-the-art supervised point-cloud network in accuracy.
  • The generated models offer objective guidance, reducing the experience dependency of orthognathic surgical planning.

Conclusions:

  • The self-supervised deep framework provides an accurate and automated solution for estimating reference facial bony shape models.
  • This approach has the potential to significantly improve the precision and outcomes of virtual orthognathic surgery.
  • The developed method offers a valuable tool for both surgical planning and medical education in craniofacial reconstruction.