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Automatic Segmentation of Mandible from Conventional Methods to Deep Learning-A Review
Bingjiang Qiu1,2,3, Hylke van der Wel1,4, Joep Kraeima1,4
13D Lab, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ Groningen, The Netherlands.
Journal of Personalized Medicine
|August 6, 2021
Summary
Accurate mandible segmentation from medical scans is crucial for oral surgery planning. This review summarizes automated methods to overcome segmentation challenges, aiding clinical applications.
Area of Science:
- Oral and Maxillofacial Surgery
- Medical Imaging
- Computer Vision
Background:
- Medical imaging is vital in oral and maxillofacial surgery (OMFS).
- Accurate mandible segmentation is essential for creating 3D models for surgical planning and analysis.
- Challenges include complex anatomy, artifacts from dental fillings or implants, and individual anatomical variations.
Purpose of the Study:
- To review and present fully and semi-automatic mandible segmentation methods.
- To provide an overview of scientific advancements in automatic mandible segmentation.
- To aid clinicians and researchers in developing new automatic segmentation methods for OMFS.
Main Methods:
- Systematic review of scientific articles on mandible segmentation.
- Focus on fully and semi-automatic computer vision algorithms.
- Analysis of methods addressing segmentation challenges in head and neck scans.
Main Results:
- Numerous automatic segmentation algorithms have been developed over the past two decades.
- These methods aim to improve accuracy and efficiency compared to manual segmentation.
- The review categorizes and describes various published approaches.
Conclusions:
- Automatic mandible segmentation is a rapidly advancing field in OMFS.
- Further development of these methods is needed for seamless clinical integration.
- This review serves as a resource for future research and clinical tool development.
