{"ID":2888548,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.00155","arxiv_id":"2508.00155","title":"GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation","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.","short_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 tailore...","url_abs":"https://arxiv.org/abs/2508.00155","url_pdf":"https://arxiv.org/pdf/2508.00155v1","authors":"[\"Tomasz Szczepański\",\"Szymon Płotka\",\"Michal K. Grzeszczyk\",\"Arleta Adamowicz\",\"Piotr Fudalej\",\"Przemysław Korzeniowski\",\"Tomasz Trzciński\",\"Arkadiusz Sitek\"]","published":"2025-07-31T20:46:58Z","proceeding":"eess.IV","tasks":"[\"eess.IV\",\"cs.AI\",\"cs.CV\",\"cs.LG\"]","methods":"[]","has_code":false,"code_links":[{"ID":611555,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2888548,"paper_url":"https://arxiv.org/abs/2508.00155","paper_title":"GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation","repo_url":"https://github.com/tomek1911/GEPAR3D","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}