GEPAR3D 3D CBCT Tooth Segmentation Dataset

This dataset accompanies the MICCAI 2025 paper titled:“GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation.” Part 1: Re-annotated Segmentation Maps (150 Volumes) - 32class_labels.zip This subset contains segmentation maps only (no imaging data), re-annotated into 32 semantic classes with Universal Numbering System based on segmentation maps introduced by Cui et al. (Nature Communications, 2022). The segmentation volumes are derived from CBCT data used in Cui et al.'s work. Imaging data (CBCT scans) are not included due to privacy and institutional data policies. Researchers interested in the corresponding raw CBCT scans must contact the original data providers: Z. Cui et al. (A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images) at [e-mail]. Data requests are subject to institutional approval and privacy review, typically within 15 working days. Cite the original Cui et al. publication when using any associated imaging data. Part 2: External Test Set (Centers A and B, Poland) - GEPAR3D_dataset.zip This part includes CBCT scans with segmentation labels from two independent Polish clinical centers, used as external test data in our MICCAI study. Data consists of anonymized CBCT volumes and voxel-wise segmentation masks for 3D tooth structures. Use of data was approved by the Institutional Review Board (IRB Approval ID: OKW-623/2022). These data were collected, processed, and labeled under ethical approval and conform to institutional privacy requirements. This dataset is released under the Non-Commercial Academic Research Use License – Redistribution Prohibited (NCRU-NR). Use of this dataset is permitted solely for non-commercial academic research purposes. Redistribution, reproduction, or adaptation of the dataset, whether in whole or in part, is strictly prohibited without prior written permission from the dataset owners. Commercial use is not permitted under this license. Users must provide appropriate attribution in any academic publications or outputs derived from the dataset. Contact: For questions, data access requests, collaborations regarding this dataset, inquiries, or support, please contact:Tomasz Szczepański: [e-mail]. Preprocessing and Annotation Details Detailed information on: Annotation protocols, Preprocessing steps (resampling, normalization), Label structures (32-class scheme), Evaluation splits (training/test IDs) … is provided in the GitHub repository accompanying the MICCAI paper - project page. We strongly recommend users consult the repository to ensure proper usage and reproducibility.Licence RequirementsWhen applying for access to this dataset, license as Non-Commercial Academic Research Use License: You accept to use this dataset for research or not commercial purposes. You will not distribute this dataset to third parties. You are the only responsable for the use you will do with this data. Citation Requirement If you use this dataset in any publication, please cite our MICCAI 2025 paper (full citation to be provided upon proceedings release). A BibTeX citation will be included in the repository and dataset page once available. @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} }本数据集配套于2025年国际医学图像计算与计算机辅助干预会议（Medical Image Computing and Computer Assisted Intervention, 简称MICCAI 2025）论文《GEPAR3D：面向三维牙齿分割的几何先验辅助学习》。 第一部分：重新标注的分割掩码（150组体积数据）- 32class_labels.zip 该子集仅包含分割掩码（无影像数据），基于Cui等人（发表于《自然-通讯》Nature Communications, 2022）提出的分割标注方案，按照通用牙位编号系统（Universal Numbering System）重新标注为32个语义类别。 这些分割体积数据源自Cui等人研究中使用的锥形束计算机断层扫描（Cone Beam Computed Tomography, 简称CBCT）影像数据。 由于隐私保护及机构数据政策，原始影像数据（CBCT扫描）未随本数据集一并发布。 如需获取对应原始CBCT扫描数据的研究者，请联系原始数据提供者： Z. Cui等人（论文《A fully automatic AI system for tooth and alveolar bone segmentation from cone-beam CT images》），邮箱：[e-mail]。 数据申请需通过机构审批及隐私审查，审批周期通常为15个工作日。 使用相关原始影像数据时，请引用Cui等人的原始发表论文。 第二部分：外部测试集（波兰A、B两家临床中心）- GEPAR3D_dataset.zip 本部分包含来自波兰两家独立临床中心的CBCT扫描影像及对应三维牙齿结构的体素级分割掩码，作为本MICCAI研究的外部测试数据使用。 该数据集包含匿名化处理后的CBCT体积数据及体素级牙齿结构分割掩码。本数据的使用已获得机构审查委员会（Institutional Review Board, 简称IRB）批准（审批编号：OKW-623/2022）。 所有数据均在伦理审批框架下收集、处理与标注，并符合机构隐私保护要求。 本数据集采用非商业学术研究使用许可-禁止再分发（NCRU-NR）协议发布。仅允许将本数据集用于非商业性学术研究用途。未经数据集所有者事先书面许可，严禁以任何形式全部或部分分发、复制或改编本数据集。本许可不允许商业使用。基于本数据集产生的任何学术出版物或成果中，必须对本数据集予以适当署名致谢。 联系方式：如有疑问、数据访问申请、相关合作、咨询或技术支持需求，请联系Tomasz Szczepański，邮箱：[e-mail]。 ### 预处理与标注详情 关于标注流程、预处理步骤（重采样、归一化）、标签结构（32类别方案）、训练/测试划分的详细信息，请参阅本论文配套的GitHub项目页面。我们强烈建议使用者查阅该仓库以确保正确使用本数据集并实现结果可复现。 #### 许可要求 在申请本数据集访问权限时，需遵守非商业学术研究使用许可条款： 1. 仅将本数据集用于非商业性研究用途； 2. 不得将本数据集分发至第三方； 3. 使用者需对自身使用本数据的行为承担全部责任。 #### 引用要求 若在任何出版物中使用本数据集，请引用本MICCAI 2025论文（完整引用信息待会议论文集发布后提供）。相关BibTeX引用格式将在仓库及数据集页面上线后公布。 bibtex @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} }