Tooth segmentation in Cone-Beam Computed Tomography (CBCT) remains challenging, especially for fine structures like root apices, which is critical for assessing root resorption in orthodontics. We introduce GEPAR3D, a novel approach that unifies instance detection and multi-class segmentation into a single step tailored to improve root segmentation. Our method integrates a Statistical Shape Model of dentition as a geometric prior, capturing anatomical context and morphological consistency without enforcing restrictive adjacency constraints. We leverage a deep watershed method, modeling each tooth as a continuous 3D energy basin encoding voxel distances to boundaries. This instance-aware representation ensures accurate segmentation of narrow, complex root apices. Trained on publicly available CBCT scans from a single center, our method is evaluated on external test sets from two in-house and two public medical centers. GEPAR3D achieves the highest overall segmentation performance, averaging a Dice Similarity Coefficient (DSC) of 95.0% (+2.8% over the second-best method) and increasing recall to 95.2% (+9.5%) across all test sets. Qualitative analyses demonstrated substantial improvements in root segmentation quality, indicating significant potential for more accurate root resorption assessment and enhanced clinical decision-making in orthodontics.
(Rotate volumes: Left mouse button; Move volumes: Shift + Left mouse button; Load scan: click bullet.)
Interactive side-by-side 3D renderings of sample segmentations (top row) and corresponding 3D surface Hausdorff distance heatmaps overlaid on ground-truth (GT) labels (bottom row). Heatmaps use green to indicate low error and purple to indicate high error (>2.0 mm), highlighting apex deviations. GEPAR3D demonstrates improved root sensitivity compared to tooth-specific baselines. Missing teeth are rendered in gray.
GEPAR3D is an encoder-decoder model with dual decoders for multi-class segmentation and instance regression. The segmentation branch classifies 32 tooth categories with Statistical Shape Model (SSM)-based regularization, while the regression branch models instances as energy basins guided by energy descent, where we adapt Deep Watershed method for 3D. Each detected instance receives class votes from the multi-class segmentation branch, and the final instance segmentation is obtained via a voting scheme. The SSM is built from a representative patient cohort, this 3D atlas encodes anatomical knowledge. By processing the SSM, we extract inter-tooth distances for morphologically guided segmentation, capturing anatomical context and morphological consistency without enforcing restrictive adjacency constraints. This integration of geometric priors and instance-aware representation ensures accurate segmentation of narrow, complex root apices.
@InProceedings{Szczepanski2025MICCAI_GEPAR3D,
author = { Szczepański, Tomasz and Płotka, Szymon and Grzeszczyk, Michal K. and Adamowicz, Arleta and Fudalej, Piotr and Korzeniowski, Przemysław and Trzciński, Tomasz and Sitek, Arkadiusz},
title = { { GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
year = {2025},
publisher = {Springer Nature Switzerland},
volume = {LNCS 15961},
month = {September},
page = {216 -- 226}
}