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[Automatic determination of mandibular landmarks based on three-dimensional mandibular average model].

Z X Gao1, Y Wang1,2, A N Wen2

  • 1Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China.

Beijing Da Xue Xue Bao. Yi Xue Ban = Journal of Peking University. Health Sciences
|January 31, 2023
PubMed
Summary
This summary is machine-generated.

This study presents an efficient, automatic method for identifying 3D mandibular anatomical landmarks using an average shape model and non-rigid registration. The technique achieves accuracy suitable for oral clinical applications, improving 3D data labeling efficiency.

Keywords:
Anatomic landmarksImaging, three-dimensionalMandibleStructural models

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Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Biomedical Engineering

Context:

  • Accurate identification of three-dimensional (3D) mandibular anatomical landmarks is crucial for craniofacial analysis and surgical planning.
  • Traditional manual landmarking is time-consuming and subject to inter-observer variability.
  • Developing automated methods is essential for improving efficiency and consistency in clinical practice.

Purpose:

  • To develop and evaluate an efficient, automatic method for determining 19 anatomical landmarks on 3D mandibular data.
  • To establish a 3D mandibular average shape model using Procrustes analysis and non-rigid registration.
  • To assess the accuracy of the automated landmarking method against expert manual identification.

Summary:

  • A 3D average mandibular model was constructed from 30 Chinese craniofacial CT datasets using Procrustes analysis.
  • A structured template with 18,996 semi-landmarks and 19 indexed landmarks was created.
  • Non-rigid registration using Meshmonk matched the template to 10 test datasets, yielding landmark positions with an average error of 1.42 mm.

Impact:

  • The automated method significantly enhances the efficiency of 3D mandibular data feature labeling.
  • The achieved accuracy is adequate for many oral and maxillofacial clinical applications.
  • Further validation is needed for the method's performance on deformed mandible data.