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Automatic virtual reconstruction of maxillofacial bone defects assisted by ICP (iterative closest point) algorithm
Bimeng Jie1,2, Boxuan Han3, Baocheng Yao1,2
1Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Road, Beijing, 100081, China.
Clinical Oral Investigations
|September 26, 2021
Summary
This study introduces an automated method using the iterative closest point (ICP) algorithm to reconstruct maxillofacial bone defects. The approach successfully predicts missing bone data, offering a feasible solution for defect reconstruction.
Area of Science:
- Biomedical Engineering
- Computer-Aided Surgery
- 3D Imaging and Modeling
Background:
- Maxillofacial bone defects pose significant reconstruction challenges.
- Current methods for virtual reconstruction can be time-consuming and require manual intervention.
Purpose of the Study:
- To develop and validate an automated approach for virtual complement and reconstruction of maxillofacial bone defects.
- To utilize the iterative closest point (ICP) algorithm for automatic defect completion.
Main Methods:
- A 3D craniomaxillofacial database of 500 normal Chinese skulls was created.
- A modified ICP algorithm was developed for automatic bone defect completion.
- Performance was evaluated through model experiments and clinical applications, measuring accuracy (RMSE) and processing time.
Main Results:
- The automated ICP algorithm achieved an average root-mean-square deviation of less than 2 mm in model experiments.
- Clinical application demonstrated a post-operative skull symmetry RMSE of 1.72 mm.
- Average processing time for the algorithm was 493.5 seconds.
Conclusions:
- The iterative closest point algorithm, combined with a normal population database, provides a feasible method for automatically predicting missing maxillofacial bone data.
- This automated approach has been successfully proposed and validated for maxillofacial bone defect reconstruction.
