GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation

• URL: http://arxiv.org/abs/2508.00155v1
• Date: Thu, 31 Jul 2025 20:46:58 GMT
• Title: GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation
• Authors: Tomasz Szczepański, Szymon Płotka, Michal K. Grzeszczyk, Arleta Adamowicz, Piotr Fudalej, Przemysław Korzeniowski, Tomasz Trzciński, Arkadiusz Sitek,
• Abstract summary: Tooth segmentation in Cone-Beam Computed Tomography (CBCT) remains challenging.<n>We introduce GEPAR3D, a novel approach that unifies instance detection and multi-class segmentation into a single step to improve root segmentation.<n>We leverage a deep watershed method, modeling each tooth as a continuous 3D energy basin encoding voxel distances to boundaries.
• Score: 0.15487122608774898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. We provide the implementation and dataset at https://github.com/tomek1911/GEPAR3D.

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