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Updated: Aug 9, 2025
Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images
Published on: January 7, 2019
Age prediction in sub-adults based on MRI segmentation of 3rd molar tissue volumes
Mai Britt Bjørk1, Sigrid Ingeborg Kvaal2, Øyvind Bleka3
1Institute of Clinical Dentistry, Faculty of Dentistry, University of Oslo, Postboks 1109, Blindern, N-00317, Oslo, Norway. mbbjork@yahoo.no.
Purpose:
Our aim was to investigate tissue volumes measured by MRI segmentation of the entire 3rd molar for prediction of a sub-adult being older than 18 years.
Material And Method:
We used a 1.5-T MR scanner with a customized high-resolution single T2 sequence acquisition with 0.37 mm iso-voxels. Two dental cotton rolls drawn with water stabilized the bite and delineated teeth from oral air. Segmentation of the different tooth tissue volumes was performed using SliceOmatic (Tomovision©). Linear regression was used to analyze the association between mathematical transformation outcomes of the tissue volumes, age, and sex. Performance of different transformation outcomes and tooth combinations were assessed based on the p value of the age variable, combined or separated for each sex depending on the selected model. The predictive probability of being older than 18 years was obtained by a Bayesian approach.
Results:
We included 67 volunteers (F/M: 45/22), range 14-24 years, median age 18 years. The transformation outcome (pulp + predentine)/total volume for upper 3rd molars had the strongest association with age (p = 3.4 × 10-9).
Conclusion:
MRI segmentation of tooth tissue volumes might prove useful in the prediction of age older than 18 years in sub-adults.
