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Detecting missing teeth on PMCT using statistical shape modeling
Dana Rahbani1, Barbara Fliss2, Lars Christian Ebert3
1Graphics and Vision Research Group (GraVis), University of Basel, Basel, Switzerland.
Forensic Science, Medicine, and Pathology
|March 9, 2023
Summary
This study presents a 3D tooth detection method using statistical shape models for forensic identification. The approach achieves high accuracy on front teeth, aiding in victim identification and dental analysis.
Area of Science:
- Forensic Dentistry
- Medical Imaging Analysis
- Computer Vision
Background:
- Accurate tooth identification in 3D medical images is crucial for victim identification and forensic investigations.
- Existing methods may struggle with incomplete or pathological dental data.
Purpose of the Study:
- To evaluate a novel tooth detection approach for mandibles using statistical shape models.
- To assess the performance of this method on diverse cases including missing teeth and pathologies.
Main Methods:
- Developed a statistical shape model from a complete mandible and teeth dataset.
- Fitted the model to target mandibles from CT images to reconstruct teeth and generate presence/absence maps.
- Evaluated accuracy on 76 diverse mandible cases.
Main Results:
- Achieved approximately 90% accuracy for front teeth (incisors, canines).
- Performance decreased for molars, particularly wisdom teeth, due to higher false-positive rates.
- The method relies solely on shape information, independent of imaging modality.
Conclusions:
- The approach provides reliable tooth count estimation (excluding wisdom teeth), identification, and reconstruction for forensic applications.
- It offers a non-modality-dependent solution applicable to various 3D scans and medical images.
- The method's target-agnostic nature allows potential adaptation for detecting missing parts in other organs.
