Abstract
Purpose: Accurate estimation of reference bony shape models is fundamental for orthognathic surgical planning. Existing methods to derive this model are of two types: one determines the reference model by estimating the deformation field to correct the patient’s deformed jaw, often introducing distortions in the predicted reference model; The other derives the reference model using a linear combination of their landmarks/vertices but overlooks the intricate nonlinear relationship between the subjects, compromising the model’s precision and quality. Methods: We have created a self-supervised learning framework to estimate the reference model. The core of this framework is a deep query network, which estimates the similarity scores between the patient’s midface and those of the normal subjects in a high-dimensional space. Subsequently, it aggregates high-dimensional features of these subjects and projects these features back to 3D structures, ultimately achieving a patient-specific reference model. Results: Our approach was trained using a dataset of 51 normal subjects and tested on 30 patient subjects to estimate their reference models. Performance assessment against the actual post-operative bone revealed a mean Chamfer distance error of 2.25 mm and an average surface distance error of 2.30 mm across the patient subjects. Conclusion: Our proposed method emphasizes the correlation between the patients and the normal subjects in a high-dimensional space, facilitating the generation of the patient-specific reference model. Both qualitative and quantitative results demonstrate its superiority over current state-of-the-art methods in reference model estimation.
Original language | English (US) |
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Pages (from-to) | 1439-1447 |
Number of pages | 9 |
Journal | International Journal of Computer Assisted Radiology and Surgery |
Volume | 19 |
Issue number | 7 |
Early online date | Jun 13 2024 |
DOIs | |
State | E-pub ahead of print - Jun 13 2024 |
Keywords
- Deep learning
- Maxillofacial deformity
- Orthognathic surgery
- Reference model prediction
- Self-supervised learning
- Surgical planning
ASJC Scopus subject areas
- Surgery
- Biomedical Engineering
- Radiology Nuclear Medicine and imaging
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Health Informatics
- Computer Graphics and Computer-Aided Design